Microsoft Malware detection¶
1.Business/Real-world Problem
1.1. What is Malware?
The term malware is a contraction of malicious software. Put simply, malware is any piece of software that was written with the intent of doing harm to data, devices or to people.
Source: https://www.avg.com/en/signal/what-is-malware
1.2. Problem Statement
In the past few years, the malware industry has grown very rapidly that, the syndicates invest heavily in technologies to evade traditional protection, forcing the anti-malware groups/communities to build more robust softwares to detect and terminate these attacks. The major part of protecting a computer system from a malware attack is to identify whether a given piece of file/software is a malware.
1.3 Source/Useful Links
Microsoft has been very active in building anti-malware products over the years and it runs it’s anti-malware utilities over 150 million computers around the world. This generates tens of millions of daily data points to be analyzed as potential malware. In order to be effective in analyzing and classifying such large amounts of data, we need to be able to group them into groups and identify their respective families.
This dataset provided by Microsoft contains about 9 classes of malware.
,
Source: https://www.kaggle.com/c/malware-classification
1.4. Real-world/Business objectives and constraints.
- Minimize multi-class error.
- Multi-class probability estimates.
- Malware detection should not take hours and block the user's computer. It should fininsh in a few seconds or a minute.
2. Machine Learning Problem
2.1. Data
2.1.1. Data Overview
- .asm file (read more: https://www.reviversoft.com/file-extensions/asm)
- .bytes file (the raw data contains the hexadecimal representation of the file's binary content, without the PE header)
- Ramnit
- Lollipop
- Kelihos_ver3
- Vundo
- Simda
- Tracur
- Kelihos_ver1
- Obfuscator.ACY
- Gatak
2.1.2. Example Data Point
.asm file
.text:00401000 assume es:nothing, ss:nothing, ds:_data, fs:nothing, gs:nothing .text:00401000 56 push esi .text:00401001 8D 44 24 08 lea eax, [esp+8] .text:00401005 50 push eax .text:00401006 8B F1 mov esi, ecx .text:00401008 E8 1C 1B 00 00 call ??0exception@std@@QAE@ABQBD@Z ; std::exception::exception(char const * const &) .text:0040100D C7 06 08 BB 42 00 mov dword ptr [esi], offset off_42BB08 .text:00401013 8B C6 mov eax, esi .text:00401015 5E pop esi .text:00401016 C2 04 00 retn 4 .text:00401016 ; --------------------------------------------------------------------------- .text:00401019 CC CC CC CC CC CC CC align 10h .text:00401020 C7 01 08 BB 42 00 mov dword ptr [ecx], offset off_42BB08 .text:00401026 E9 26 1C 00 00 jmp sub_402C51 .text:00401026 ; --------------------------------------------------------------------------- .text:0040102B CC CC CC CC CC align 10h .text:00401030 56 push esi .text:00401031 8B F1 mov esi, ecx .text:00401033 C7 06 08 BB 42 00 mov dword ptr [esi], offset off_42BB08 .text:00401039 E8 13 1C 00 00 call sub_402C51 .text:0040103E F6 44 24 08 01 test byte ptr [esp+8], 1 .text:00401043 74 09 jz short loc_40104E .text:00401045 56 push esi .text:00401046 E8 6C 1E 00 00 call ??3@YAXPAX@Z ; operator delete(void *) .text:0040104B 83 C4 04 add esp, 4 .text:0040104E .text:0040104E loc_40104E: ; CODE XREF: .text:00401043j .text:0040104E 8B C6 mov eax, esi .text:00401050 5E pop esi .text:00401051 C2 04 00 retn 4 .text:00401051 ; ---------------------------------------------------------------------------
.bytes file
00401000 00 00 80 40 40 28 00 1C 02 42 00 C4 00 20 04 20 00401010 00 00 20 09 2A 02 00 00 00 00 8E 10 41 0A 21 01 00401020 40 00 02 01 00 90 21 00 32 40 00 1C 01 40 C8 18 00401030 40 82 02 63 20 00 00 09 10 01 02 21 00 82 00 04 00401040 82 20 08 83 00 08 00 00 00 00 02 00 60 80 10 80 00401050 18 00 00 20 A9 00 00 00 00 04 04 78 01 02 70 90 00401060 00 02 00 08 20 12 00 00 00 40 10 00 80 00 40 19 00401070 00 00 00 00 11 20 80 04 80 10 00 20 00 00 25 00 00401080 00 00 01 00 00 04 00 10 02 C1 80 80 00 20 20 00 00401090 08 A0 01 01 44 28 00 00 08 10 20 00 02 08 00 00 004010A0 00 40 00 00 00 34 40 40 00 04 00 08 80 08 00 08 004010B0 10 00 40 00 68 02 40 04 E1 00 28 14 00 08 20 0A 004010C0 06 01 02 00 40 00 00 00 00 00 00 20 00 02 00 04 004010D0 80 18 90 00 00 10 A0 00 45 09 00 10 04 40 44 82 004010E0 90 00 26 10 00 00 04 00 82 00 00 00 20 40 00 00 004010F0 B4 00 00 40 00 02 20 25 08 00 00 00 00 00 00 00 00401100 08 00 00 50 00 08 40 50 00 02 06 22 08 85 30 00 00401110 00 80 00 80 60 00 09 00 04 20 00 00 00 00 00 00 00401120 00 82 40 02 00 11 46 01 4A 01 8C 01 E6 00 86 10 00401130 4C 01 22 00 64 00 AE 01 EA 01 2A 11 E8 10 26 11 00401140 4E 11 8E 11 C2 00 6C 00 0C 11 60 01 CA 00 62 10 00401150 6C 01 A0 11 CE 10 2C 11 4E 10 8C 00 CE 01 AE 01 00401160 6C 10 6C 11 A2 01 AE 00 46 11 EE 10 22 00 A8 00 00401170 EC 01 08 11 A2 01 AE 10 6C 00 6E 00 AC 11 8C 00 00401180 EC 01 2A 10 2A 01 AE 00 40 00 C8 10 48 01 4E 11 00401190 0E 00 EC 11 24 10 4A 10 04 01 C8 11 E6 01 C2 00
2.2. Mapping the real-world problem to an ML problem
2.2.1. Type of Machine Learning Problem
There are nine different classes of malware that we need to classify a given a data point => Multi class classification problem
2.2.2. Performance Metric
Source: https://www.kaggle.com/c/malware-classification#evaluation
Metric(s):
- Multi class log-loss
- Confusion matrix
2.2.3. Machine Learing Objectives and Constraints
Objective: Predict the probability of each data-point belonging to each of the nine classes.
Constraints:
- Class probabilities are needed.
- Penalize the errors in class probabilites => Metric is Log-loss.
- Some Latency constraints.
2.3. Train and Test Dataset
Split the dataset randomly into three parts train, cross validation and test with 64%,16%, 20% of data respectively
2.4. Useful blogs, videos and reference papers
http://blog.kaggle.com/2015/05/26/microsoft-malware-winners-interview-1st-place-no-to-overfitting/
https://arxiv.org/pdf/1511.04317.pdf
First place solution in Kaggle competition: https://www.youtube.com/watch?v=VLQTRlLGz5Y
https://github.com/dchad/malware-detection
http://vizsec.org/files/2011/Nataraj.pdf
https://www.dropbox.com/sh/gfqzv0ckgs4l1bf/AAB6EelnEjvvuQg2nu_pIB6ua?dl=0
" Cross validation is more trustworthy than domain knowledge."
Mounting Drive¶
In [0]:
!kill -9 -1
In [2]:
from google.colab import drive
drive.mount('/content/drive')
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code Enter your authorization code: ·········· Mounted at /content/drive
In [3]:
!pwd
!ls
/content drive sample_data
In [4]:
import os
PATH = os.getcwd()
print(PATH)
/content
In [5]:
data_path = PATH + '/drive/My Drive/AAIC/Case Studies/Microsoft Malware Detection/'
data_path
Out[5]:
'/content/drive/My Drive/AAIC/Case Studies/Microsoft Malware Detection/'
3. Exploratory Data Analysis
In [0]:
import warnings
warnings.filterwarnings("ignore")
import shutil
import os
import pandas as pd
import matplotlib
matplotlib.use(u'nbAgg')
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pickle
from sklearn.manifold import TSNE
from sklearn import preprocessing
import pandas as pd
from multiprocessing import Process# this is used for multithreading
import multiprocessing
import codecs# this is used for file operations
import random as r
from xgboost import XGBClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import log_loss
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
In [0]:
#separating byte files and asm files
source = 'train'
destination = 'byteFiles'
# we will check if the folder 'byteFiles' exists if it not there we will create a folder with the same name
if not os.path.isdir(destination):
os.makedirs(destination)
# if we have folder called 'train' (train folder contains both .asm files and .bytes files) we will rename it 'asmFiles'
# for every file that we have in our 'asmFiles' directory we check if it is ending with .bytes, if yes we will move it to
# 'byteFiles' folder
# so by the end of this snippet we will separate all the .byte files and .asm files
if os.path.isdir(source):
os.rename(source,'asmFiles')
source='asmFiles'
data_files = os.listdir(source)
for file in data_files:
print(file)
if (file.endswith("bytes")):
shutil.move(source+'\\'+file,destination)
01azqd4InC7m9JpocGv5.asm 01azqd4InC7m9JpocGv5.bytes 01IsoiSMh5gxyDYTl4CB.asm 01IsoiSMh5gxyDYTl4CB.bytes 01jsnpXSAlgw6aPeDxrU.asm 01jsnpXSAlgw6aPeDxrU.bytes 01kcPWA9K2BOxQeS5Rju.asm 01kcPWA9K2BOxQeS5Rju.bytes 01SuzwMJEIXsK7A8dQbl.asm 01SuzwMJEIXsK7A8dQbl.bytes 02IOCvYEy8mjiuAQHax3.asm 02IOCvYEy8mjiuAQHax3.bytes 02JqQ7H3yEoD8viYWlmS.asm 02JqQ7H3yEoD8viYWlmS.bytes 02K5GMYITj7bBoAisEmD.asm 02K5GMYITj7bBoAisEmD.bytes 02mlBLHZTDFXGa7Nt6cr.asm 02mlBLHZTDFXGa7Nt6cr.bytes 02MRILoE6rNhmt7FUi45.asm 02MRILoE6rNhmt7FUi45.bytes 02zcUmKV16Lya5xqnPGB.asm 02zcUmKV16Lya5xqnPGB.bytes 03nJaQV6K2ObICUmyWoR.asm 03nJaQV6K2ObICUmyWoR.bytes 04BfoQRA6XEshiNuI7pF.asm 04BfoQRA6XEshiNuI7pF.bytes 04cvLCVPqBMs6yn5xGlE.asm 04cvLCVPqBMs6yn5xGlE.bytes 04EjIdbPV5e1XroFOpiN.asm 04EjIdbPV5e1XroFOpiN.bytes 04hSzLv5s2TDYPlcgpHB.asm 04hSzLv5s2TDYPlcgpHB.bytes 04mcPSei852tgIKUwTJr.asm 04mcPSei852tgIKUwTJr.bytes 04QzZ3DVdPsEp9elLR65.asm 04QzZ3DVdPsEp9elLR65.bytes 04sJnMaORYc1SV5pKjrP.asm 04sJnMaORYc1SV5pKjrP.bytes 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3.1. Distribution of malware classes in whole data set
In [0]:
Y=pd.read_csv("trainLabels.csv")
total = len(Y)*1.
ax=sns.countplot(x="Class", data=Y)
for p in ax.patches:
ax.annotate('{:.1f}%'.format(100*p.get_height()/total), (p.get_x()+0.1, p.get_height()+5))
#put 11 ticks (therefore 10 steps), from 0 to the total number of rows in the dataframe
ax.yaxis.set_ticks(np.linspace(0, total, 11))
#adjust the ticklabel to the desired format, without changing the position of the ticks.
ax.set_yticklabels(map('{:.1f}%'.format, 100*ax.yaxis.get_majorticklocs()/total))
plt.show()
3.2. Feature extraction
3.2.1 File size of byte files as a feature
In [0]:
#file sizes of byte files
files=os.listdir('byteFiles')
filenames=Y['Id'].tolist()
class_y=Y['Class'].tolist()
class_bytes=[]
sizebytes=[]
fnames=[]
for file in files:
# print(os.stat('byteFiles/0A32eTdBKayjCWhZqDOQ.txt'))
# os.stat_result(st_mode=33206, st_ino=1125899906874507, st_dev=3561571700, st_nlink=1, st_uid=0, st_gid=0,
# st_size=3680109, st_atime=1519638522, st_mtime=1519638522, st_ctime=1519638522)
# read more about os.stat: here https://www.tutorialspoint.com/python/os_stat.htm
statinfo=os.stat('byteFiles/'+file)
# split the file name at '.' and take the first part of it i.e the file name
file=file.split('.')[0]
if any(file == filename for filename in filenames):
i=filenames.index(file)
class_bytes.append(class_y[i])
# converting into Mb's
sizebytes.append(statinfo.st_size/(1024.0*1024.0))
fnames.append(file)
data_size_byte=pd.DataFrame({'ID':fnames,'size':sizebytes,'Class':class_bytes})
print (data_size_byte.head())
ID size Class 0 01azqd4InC7m9JpocGv5 4.234863 9 1 01IsoiSMh5gxyDYTl4CB 5.538818 2 2 01jsnpXSAlgw6aPeDxrU 3.887939 9 3 01kcPWA9K2BOxQeS5Rju 0.574219 1 4 01SuzwMJEIXsK7A8dQbl 0.370850 8
3.2.2 box plots of file size (.byte files) feature
In [0]:
#boxplot of byte files
ax = sns.boxplot(x="Class", y="size", data=data_size_byte)
plt.title("boxplot of .bytes file sizes")
plt.show()
3.2.3 feature extraction from byte files
In [0]:
#removal of addres from byte files
# contents of .byte files
# ----------------
#00401000 56 8D 44 24 08 50 8B F1 E8 1C 1B 00 00 C7 06 08
#-------------------
#we remove the starting address 00401000
files = os.listdir('byteFiles')
filenames=[]
array=[]
for file in files:
if(file.endswith("bytes")):
file=file.split('.')[0]
text_file = open('byteFiles/'+file+".txt", 'w+')
with open('byteFiles/'+file+".bytes","r") as fp:
lines=""
for line in fp:
a=line.rstrip().split(" ")[1:]
b=' '.join(a)
b=b+"\n"
text_file.write(b)
fp.close()
os.remove('byteFiles/'+file+".bytes")
text_file.close()
files = os.listdir('byteFiles')
filenames2=[]
feature_matrix = np.zeros((len(files),257),dtype=int)
k=0
#program to convert into bag of words of bytefiles
#this is custom-built bag of words this is unigram bag of words
byte_feature_file=open('result.csv','w+')
byte_feature_file.write("ID,0,1,2,3,4,5,6,7,8,9,0a,0b,0c,0d,0e,0f,10,11,12,13,14,15,16,17,18,19,1a,1b,1c,1d,1e,1f,20,21,22,23,24,25,26,27,28,29,2a,2b,2c,2d,2e,2f,30,31,32,33,34,35,36,37,38,39,3a,3b,3c,3d,3e,3f,40,41,42,43,44,45,46,47,48,49,4a,4b,4c,4d,4e,4f,50,51,52,53,54,55,56,57,58,59,5a,5b,5c,5d,5e,5f,60,61,62,63,64,65,66,67,68,69,6a,6b,6c,6d,6e,6f,70,71,72,73,74,75,76,77,78,79,7a,7b,7c,7d,7e,7f,80,81,82,83,84,85,86,87,88,89,8a,8b,8c,8d,8e,8f,90,91,92,93,94,95,96,97,98,99,9a,9b,9c,9d,9e,9f,a0,a1,a2,a3,a4,a5,a6,a7,a8,a9,aa,ab,ac,ad,ae,af,b0,b1,b2,b3,b4,b5,b6,b7,b8,b9,ba,bb,bc,bd,be,bf,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,ca,cb,cc,cd,ce,cf,d0,d1,d2,d3,d4,d5,d6,d7,d8,d9,da,db,dc,dd,de,df,e0,e1,e2,e3,e4,e5,e6,e7,e8,e9,ea,eb,ec,ed,ee,ef,f0,f1,f2,f3,f4,f5,f6,f7,f8,f9,fa,fb,fc,fd,fe,ff,??")
byte_feature_file.write("\n")
for file in files:
filenames2.append(file)
byte_feature_file.write(file+",")
if(file.endswith("txt")):
with open('byteFiles/'+file,"r") as byte_flie:
for lines in byte_flie:
line=lines.rstrip().split(" ")
for hex_code in line:
if hex_code=='??':
feature_matrix[k][256]+=1
else:
feature_matrix[k][int(hex_code,16)]+=1
byte_flie.close()
for i, row in enumerate(feature_matrix[k]):
if i!=len(feature_matrix[k])-1:
byte_feature_file.write(str(row)+",")
else:
byte_feature_file.write(str(row))
byte_feature_file.write("\n")
k += 1
byte_feature_file.close()
In [0]:
byte_features=pd.read_csv("result.csv")
byte_features['ID'] = byte_features['ID'].str.split('.').str[0]
byte_features.head(2)
Out[0]:
| ID | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ... | f7 | f8 | f9 | fa | fb | fc | fd | fe | ff | ?? | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 601905 | 3905 | 2816 | 3832 | 3345 | 3242 | 3650 | 3201 | 2965 | ... | 2804 | 3687 | 3101 | 3211 | 3097 | 2758 | 3099 | 2759 | 5753 | 1824 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 39755 | 8337 | 7249 | 7186 | 8663 | 6844 | 8420 | 7589 | 9291 | ... | 451 | 6536 | 439 | 281 | 302 | 7639 | 518 | 17001 | 54902 | 8588 |
2 rows × 258 columns
In [0]:
data_size_byte.head(2)
Out[0]:
| ID | size | Class | |
|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 4.234863 | 9 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 5.538818 | 2 |
In [0]:
byte_features_with_size = byte_features.merge(data_size_byte, on='ID')
byte_features_with_size.to_csv("result_with_size.csv")
byte_features_with_size.head(2)
Out[0]:
| ID | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ... | f9 | fa | fb | fc | fd | fe | ff | ?? | size | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 601905 | 3905 | 2816 | 3832 | 3345 | 3242 | 3650 | 3201 | 2965 | ... | 3101 | 3211 | 3097 | 2758 | 3099 | 2759 | 5753 | 1824 | 4.234863 | 9 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 39755 | 8337 | 7249 | 7186 | 8663 | 6844 | 8420 | 7589 | 9291 | ... | 439 | 281 | 302 | 7639 | 518 | 17001 | 54902 | 8588 | 5.538818 | 2 |
2 rows × 260 columns
In [0]:
# https://stackoverflow.com/a/29651514
def normalize(df):
result1 = df.copy()
for feature_name in df.columns:
if (str(feature_name) != str('ID') and str(feature_name)!=str('Class')):
max_value = df[feature_name].max()
min_value = df[feature_name].min()
result1[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)
return result1
result = normalize(byte_features_with_size)
In [0]:
result.head(2)
Out[0]:
| ID | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ... | f9 | fa | fb | fc | fd | fe | ff | ?? | size | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 0.262806 | 0.005498 | 0.001567 | 0.002067 | 0.002048 | 0.001835 | 0.002058 | 0.002946 | 0.002638 | ... | 0.01356 | 0.013107 | 0.013634 | 0.031724 | 0.014549 | 0.014348 | 0.007843 | 0.000129 | 0.092219 | 9 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 0.017358 | 0.011737 | 0.004033 | 0.003876 | 0.005303 | 0.003873 | 0.004747 | 0.006984 | 0.008267 | ... | 0.00192 | 0.001147 | 0.001329 | 0.087867 | 0.002432 | 0.088411 | 0.074851 | 0.000606 | 0.121236 | 2 |
2 rows × 260 columns
In [0]:
data_y = result['Class']
result.head()
Out[0]:
| ID | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ... | f9 | fa | fb | fc | fd | fe | ff | ?? | Class | size | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 0.262806 | 0.005498 | 0.001567 | 0.002067 | 0.002048 | 0.001835 | 0.002058 | 0.002946 | 0.002638 | ... | 0.013560 | 0.013107 | 0.013634 | 0.031724 | 0.014549 | 0.014348 | 0.007843 | 0.000129 | 9 | 0.092219 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 0.017358 | 0.011737 | 0.004033 | 0.003876 | 0.005303 | 0.003873 | 0.004747 | 0.006984 | 0.008267 | ... | 0.001920 | 0.001147 | 0.001329 | 0.087867 | 0.002432 | 0.088411 | 0.074851 | 0.000606 | 2 | 0.121236 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 0.040827 | 0.013434 | 0.001429 | 0.001315 | 0.005464 | 0.005280 | 0.005078 | 0.002155 | 0.008104 | ... | 0.009804 | 0.011777 | 0.012604 | 0.028423 | 0.013080 | 0.013937 | 0.067001 | 0.000033 | 9 | 0.084499 |
| 3 | 01kcPWA9K2BOxQeS5Rju | 0.009209 | 0.001708 | 0.000404 | 0.000441 | 0.000770 | 0.000354 | 0.000310 | 0.000481 | 0.000959 | ... | 0.002121 | 0.001886 | 0.002272 | 0.013032 | 0.002211 | 0.003957 | 0.010904 | 0.000984 | 1 | 0.010759 |
| 4 | 01SuzwMJEIXsK7A8dQbl | 0.008629 | 0.001000 | 0.000168 | 0.000234 | 0.000342 | 0.000232 | 0.000148 | 0.000229 | 0.000376 | ... | 0.001530 | 0.000853 | 0.001052 | 0.007511 | 0.001038 | 0.001258 | 0.002998 | 0.000636 | 8 | 0.006233 |
5 rows × 260 columns
3.2.4 Multivariate Analysis
In [0]:
#multivariate analysis on byte files
#this is with perplexity 50
xtsne=TSNE(perplexity=50)
results=xtsne.fit_transform(result.drop(['ID','Class'], axis=1))
vis_x = results[:, 0]
vis_y = results[:, 1]
plt.scatter(vis_x, vis_y, c=data_y, cmap=plt.cm.get_cmap("jet", 9))
plt.colorbar(ticks=range(10))
plt.clim(0.5, 9)
plt.show()
In [0]:
#this is with perplexity 30
xtsne=TSNE(perplexity=30)
results=xtsne.fit_transform(result.drop(['ID','Class'], axis=1))
vis_x = results[:, 0]
vis_y = results[:, 1]
plt.scatter(vis_x, vis_y, c=data_y, cmap=plt.cm.get_cmap("jet", 9))
plt.colorbar(ticks=range(10))
plt.clim(0.5, 9)
plt.show()
Train Test split¶
In [0]:
data_y = result['Class']
# split the data into test and train by maintaining same distribution of output varaible 'y_true' [stratify=y_true]
X_train, X_test, y_train, y_test = train_test_split(result.drop(['ID','Class'], axis=1), data_y,stratify=data_y,test_size=0.20)
# split the train data into train and cross validation by maintaining same distribution of output varaible 'y_train' [stratify=y_train]
X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train,stratify=y_train,test_size=0.20)
In [0]:
print('Number of data points in train data:', X_train.shape[0])
print('Number of data points in test data:', X_test.shape[0])
print('Number of data points in cross validation data:', X_cv.shape[0])
Number of data points in train data: 6955 Number of data points in test data: 2174 Number of data points in cross validation data: 1739
In [0]:
# it returns a dict, keys as class labels and values as the number of data points in that class
train_class_distribution = y_train.value_counts().sortlevel()
test_class_distribution = y_test.value_counts().sortlevel()
cv_class_distribution = y_cv.value_counts().sortlevel()
my_colors = 'rgbkymc'
train_class_distribution.plot(kind='bar', color=my_colors)
plt.xlabel('Class')
plt.ylabel('Data points per Class')
plt.title('Distribution of yi in train data')
plt.grid()
plt.show()
# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
# -(train_class_distribution.values): the minus sign will give us in decreasing order
sorted_yi = np.argsort(-train_class_distribution.values)
for i in sorted_yi:
print('Number of data points in class', i+1, ':',train_class_distribution.values[i], '(', np.round((train_class_distribution.values[i]/y_train.shape[0]*100), 3), '%)')
print('-'*80)
my_colors = 'rgbkymc'
test_class_distribution.plot(kind='bar', color=my_colors)
plt.xlabel('Class')
plt.ylabel('Data points per Class')
plt.title('Distribution of yi in test data')
plt.grid()
plt.show()
# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
# -(train_class_distribution.values): the minus sign will give us in decreasing order
sorted_yi = np.argsort(-test_class_distribution.values)
for i in sorted_yi:
print('Number of data points in class', i+1, ':',test_class_distribution.values[i], '(', np.round((test_class_distribution.values[i]/y_test.shape[0]*100), 3), '%)')
print('-'*80)
my_colors = 'rgbkymc'
cv_class_distribution.plot(kind='bar', color=my_colors)
plt.xlabel('Class')
plt.ylabel('Data points per Class')
plt.title('Distribution of yi in cross validation data')
plt.grid()
plt.show()
# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
# -(train_class_distribution.values): the minus sign will give us in decreasing order
sorted_yi = np.argsort(-train_class_distribution.values)
for i in sorted_yi:
print('Number of data points in class', i+1, ':',cv_class_distribution.values[i], '(', np.round((cv_class_distribution.values[i]/y_cv.shape[0]*100), 3), '%)')
Number of data points in class 3 : 1883 ( 27.074 %) Number of data points in class 2 : 1586 ( 22.804 %) Number of data points in class 1 : 986 ( 14.177 %) Number of data points in class 8 : 786 ( 11.301 %) Number of data points in class 9 : 648 ( 9.317 %) Number of data points in class 6 : 481 ( 6.916 %) Number of data points in class 4 : 304 ( 4.371 %) Number of data points in class 7 : 254 ( 3.652 %) Number of data points in class 5 : 27 ( 0.388 %) --------------------------------------------------------------------------------
Number of data points in class 3 : 588 ( 27.047 %) Number of data points in class 2 : 496 ( 22.815 %) Number of data points in class 1 : 308 ( 14.167 %) Number of data points in class 8 : 246 ( 11.316 %) Number of data points in class 9 : 203 ( 9.338 %) Number of data points in class 6 : 150 ( 6.9 %) Number of data points in class 4 : 95 ( 4.37 %) Number of data points in class 7 : 80 ( 3.68 %) Number of data points in class 5 : 8 ( 0.368 %) --------------------------------------------------------------------------------
Number of data points in class 3 : 471 ( 27.085 %) Number of data points in class 2 : 396 ( 22.772 %) Number of data points in class 1 : 247 ( 14.204 %) Number of data points in class 8 : 196 ( 11.271 %) Number of data points in class 9 : 162 ( 9.316 %) Number of data points in class 6 : 120 ( 6.901 %) Number of data points in class 4 : 76 ( 4.37 %) Number of data points in class 7 : 64 ( 3.68 %) Number of data points in class 5 : 7 ( 0.403 %)
In [0]:
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
print("Number of misclassified points ",(len(test_y)-np.trace(C))/len(test_y)*100)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
cmap=sns.light_palette("green")
# representing A in heatmap format
print("-"*50, "Confusion matrix", "-"*50)
plt.figure(figsize=(10,5))
sns.heatmap(C, annot=True, cmap=cmap, fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*50, "Precision matrix", "-"*50)
plt.figure(figsize=(10,5))
sns.heatmap(B, annot=True, cmap=cmap, fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("Sum of columns in precision matrix",B.sum(axis=0))
# representing B in heatmap format
print("-"*50, "Recall matrix" , "-"*50)
plt.figure(figsize=(10,5))
sns.heatmap(A, annot=True, cmap=cmap, fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("Sum of rows in precision matrix",A.sum(axis=1))
4. Machine Learning Models
4.1. Machine Leaning Models on bytes files
4.1.1. Random Model
In [0]:
# we need to generate 9 numbers and the sum of numbers should be 1
# one solution is to genarate 9 numbers and divide each of the numbers by their sum
# ref: https://stackoverflow.com/a/18662466/4084039
test_data_len = X_test.shape[0]
cv_data_len = X_cv.shape[0]
# we create a output array that has exactly same size as the CV data
cv_predicted_y = np.zeros((cv_data_len,9))
for i in range(cv_data_len):
rand_probs = np.random.rand(1,9)
cv_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])
print("Log loss on Cross Validation Data using Random Model",log_loss(y_cv,cv_predicted_y, eps=1e-15))
# Test-Set error.
#we create a output array that has exactly same as the test data
test_predicted_y = np.zeros((test_data_len,9))
for i in range(test_data_len):
rand_probs = np.random.rand(1,9)
test_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])
print("Log loss on Test Data using Random Model",log_loss(y_test,test_predicted_y, eps=1e-15))
predicted_y =np.argmax(test_predicted_y, axis=1)
plot_confusion_matrix(y_test, predicted_y+1)
Log loss on Cross Validation Data using Random Model 2.45615644965 Log loss on Test Data using Random Model 2.48503905509 Number of misclassified points 88.5004599816 -------------------------------------------------- Confusion matrix --------------------------------------------------
-------------------------------------------------- Precision matrix --------------------------------------------------
Sum of columns in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.] -------------------------------------------------- Recall matrix --------------------------------------------------
Sum of rows in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.]
4.1.2. K Nearest Neighbour Classification
In [0]:
# find more about KNeighborsClassifier() here http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
# -------------------------
# default parameter
# KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2,
# metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)
# methods of
# fit(X, y) : Fit the model using X as training data and y as target values
# predict(X):Predict the class labels for the provided data
# predict_proba(X):Return probability estimates for the test data X.
#-------------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/k-nearest-neighbors-geometric-intuition-with-a-toy-example-1/
#-------------------------------------
# find more about CalibratedClassifierCV here at http://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html
# ----------------------------
# default paramters
# sklearn.calibration.CalibratedClassifierCV(base_estimator=None, method=’sigmoid’, cv=3)
#
# some of the methods of CalibratedClassifierCV()
# fit(X, y[, sample_weight]) Fit the calibrated model
# get_params([deep]) Get parameters for this estimator.
# predict(X) Predict the target of new samples.
# predict_proba(X) Posterior probabilities of classification
#-------------------------------------
# video link:
#-------------------------------------
alpha = [x for x in range(1, 15, 2)]
cv_log_error_array=[]
for i in alpha:
k_cfl=KNeighborsClassifier(n_neighbors=i)
k_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(k_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_cv)
cv_log_error_array.append(log_loss(y_cv, predict_y, labels=k_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for k = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
k_cfl=KNeighborsClassifier(n_neighbors=alpha[best_alpha])
k_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(k_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_train)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train, predict_y))
predict_y = sig_clf.predict_proba(X_cv)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv, predict_y))
predict_y = sig_clf.predict_proba(X_test)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test, predict_y))
plot_confusion_matrix(y_test, sig_clf.predict(X_test))
log_loss for k = 1 is 0.225386237304 log_loss for k = 3 is 0.230795229168 log_loss for k = 5 is 0.252421408646 log_loss for k = 7 is 0.273827486888 log_loss for k = 9 is 0.286469181555 log_loss for k = 11 is 0.29623391147 log_loss for k = 13 is 0.307551203154
For values of best alpha = 1 The train log loss is: 0.0782947669247 For values of best alpha = 1 The cross validation log loss is: 0.225386237304 For values of best alpha = 1 The test log loss is: 0.241508604195 Number of misclassified points 4.50781968721 -------------------------------------------------- Confusion matrix --------------------------------------------------
-------------------------------------------------- Precision matrix --------------------------------------------------
Sum of columns in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.] -------------------------------------------------- Recall matrix --------------------------------------------------
Sum of rows in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.]
4.1.3. Logistic Regression
In [0]:
# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
# ------------------------------
# default parameters
# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None,
# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5,
# class_weight=None, warm_start=False, average=False, n_iter=None)
# some of methods
# fit(X, y[, coef_init, intercept_init, …]) Fit linear model with Stochastic Gradient Descent.
# predict(X) Predict class labels for samples in X.
#-------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/geometric-intuition-1/
#------------------------------
alpha = [10 ** x for x in range(-5, 4)]
cv_log_error_array=[]
for i in alpha:
logisticR=LogisticRegression(penalty='l2',C=i,class_weight='balanced')
logisticR.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(logisticR, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_cv)
cv_log_error_array.append(log_loss(y_cv, predict_y, labels=logisticR.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
logisticR=LogisticRegression(penalty='l2',C=alpha[best_alpha],class_weight='balanced')
logisticR.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(logisticR, method="sigmoid")
sig_clf.fit(X_train, y_train)
pred_y=sig_clf.predict(X_test)
predict_y = sig_clf.predict_proba(X_train)
print ('log loss for train data',log_loss(y_train, predict_y, labels=logisticR.classes_, eps=1e-15))
predict_y = sig_clf.predict_proba(X_cv)
print ('log loss for cv data',log_loss(y_cv, predict_y, labels=logisticR.classes_, eps=1e-15))
predict_y = sig_clf.predict_proba(X_test)
print ('log loss for test data',log_loss(y_test, predict_y, labels=logisticR.classes_, eps=1e-15))
plot_confusion_matrix(y_test, sig_clf.predict(X_test))
log_loss for c = 1e-05 is 1.56916911178 log_loss for c = 0.0001 is 1.57336384417 log_loss for c = 0.001 is 1.53598598273 log_loss for c = 0.01 is 1.01720972418 log_loss for c = 0.1 is 0.857766083873 log_loss for c = 1 is 0.711154393309 log_loss for c = 10 is 0.583929522635 log_loss for c = 100 is 0.549929846589 log_loss for c = 1000 is 0.624746769121
log loss for train data 0.498923428696 log loss for cv data 0.549929846589 log loss for test data 0.528347316704 Number of misclassified points 12.3275068997 -------------------------------------------------- Confusion matrix --------------------------------------------------
-------------------------------------------------- Precision matrix --------------------------------------------------
Sum of columns in precision matrix [ 1. 1. 1. 1. nan 1. 1. 1. 1.] -------------------------------------------------- Recall matrix --------------------------------------------------
Sum of rows in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.]
4.1.4. Random Forest Classifier
In [0]:
# --------------------------------
# default parameters
# sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion=’gini’, max_depth=None, min_samples_split=2,
# min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0,
# min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False,
# class_weight=None)
# Some of methods of RandomForestClassifier()
# fit(X, y, [sample_weight]) Fit the SVM model according to the given training data.
# predict(X) Perform classification on samples in X.
# predict_proba (X) Perform classification on samples in X.
# some of attributes of RandomForestClassifier()
# feature_importances_ : array of shape = [n_features]
# The feature importances (the higher, the more important the feature).
# --------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/random-forest-and-their-construction-2/
# --------------------------------
alpha=[10,50,100,500,1000,2000,3000]
cv_log_error_array=[]
train_log_error_array=[]
from sklearn.ensemble import RandomForestClassifier
for i in alpha:
r_cfl=RandomForestClassifier(n_estimators=i,random_state=42,n_jobs=-1)
r_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_cv)
cv_log_error_array.append(log_loss(y_cv, predict_y, labels=r_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
r_cfl=RandomForestClassifier(n_estimators=alpha[best_alpha],random_state=42,n_jobs=-1)
r_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_train)
print('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train, predict_y))
predict_y = sig_clf.predict_proba(X_cv)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv, predict_y))
predict_y = sig_clf.predict_proba(X_test)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test, predict_y))
plot_confusion_matrix(y_test, sig_clf.predict(X_test))
log_loss for c = 10 is 0.106357709164 log_loss for c = 50 is 0.0902124124145 log_loss for c = 100 is 0.0895043339776 log_loss for c = 500 is 0.0881420869288 log_loss for c = 1000 is 0.0879849524621 log_loss for c = 2000 is 0.0881566647295 log_loss for c = 3000 is 0.0881318948443
For values of best alpha = 1000 The train log loss is: 0.0266476291801 For values of best alpha = 1000 The cross validation log loss is: 0.0879849524621 For values of best alpha = 1000 The test log loss is: 0.0858346961407 Number of misclassified points 2.02391904324 -------------------------------------------------- Confusion matrix --------------------------------------------------
-------------------------------------------------- Precision matrix --------------------------------------------------
Sum of columns in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.] -------------------------------------------------- Recall matrix --------------------------------------------------
Sum of rows in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.]
4.1.5. XgBoost Classification
In [0]:
# Training a hyper-parameter tuned Xg-Boost regressor on our train data
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link1: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/regression-using-decision-trees-2/
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
alpha=[10,50,100,500,1000,2000]
cv_log_error_array=[]
for i in alpha:
x_cfl=XGBClassifier(n_estimators=i,nthread=-1)
x_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_cv)
cv_log_error_array.append(log_loss(y_cv, predict_y, labels=x_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
x_cfl=XGBClassifier(n_estimators=alpha[best_alpha],nthread=-1)
x_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_train)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train, predict_y))
predict_y = sig_clf.predict_proba(X_cv)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv, predict_y))
predict_y = sig_clf.predict_proba(X_test)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test, predict_y))
plot_confusion_matrix(y_test, sig_clf.predict(X_test))
log_loss for c = 10 is 0.20615980494 log_loss for c = 50 is 0.123888382365 log_loss for c = 100 is 0.099919437112 log_loss for c = 500 is 0.0931035681289 log_loss for c = 1000 is 0.0933084876012 log_loss for c = 2000 is 0.0938395690309
For values of best alpha = 500 The train log loss is: 0.0225231805824 For values of best alpha = 500 The cross validation log loss is: 0.0931035681289 For values of best alpha = 500 The test log loss is: 0.0792067651731 Number of misclassified points 1.24195032199 -------------------------------------------------- Confusion matrix --------------------------------------------------
-------------------------------------------------- Precision matrix --------------------------------------------------
Sum of columns in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.] -------------------------------------------------- Recall matrix --------------------------------------------------
Sum of rows in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.]
4.1.5. XgBoost Classification with best hyper parameters using RandomSearch
In [0]:
# https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
x_cfl=XGBClassifier()
prams={
'learning_rate':[0.01,0.03,0.05,0.1,0.15,0.2],
'n_estimators':[100,200,500,1000,2000],
'max_depth':[3,5,10],
'colsample_bytree':[0.1,0.3,0.5,1],
'subsample':[0.1,0.3,0.5,1]
}
random_cfl1=RandomizedSearchCV(x_cfl,param_distributions=prams,verbose=10,n_jobs=-1,)
random_cfl1.fit(X_train,y_train)
Fitting 3 folds for each of 10 candidates, totalling 30 fits
[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 26.5s [Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 5.8min [Parallel(n_jobs=-1)]: Done 19 out of 30 | elapsed: 9.3min remaining: 5.4min [Parallel(n_jobs=-1)]: Done 23 out of 30 | elapsed: 10.1min remaining: 3.1min [Parallel(n_jobs=-1)]: Done 27 out of 30 | elapsed: 14.0min remaining: 1.6min [Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 14.2min finished
Out[0]:
RandomizedSearchCV(cv=None, error_score='raise',
estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,
gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,
min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
objective='binary:logistic', reg_alpha=0, reg_lambda=1,
scale_pos_weight=1, seed=0, silent=True, subsample=1),
fit_params=None, iid=True, n_iter=10, n_jobs=-1,
param_distributions={'learning_rate': [0.01, 0.03, 0.05, 0.1, 0.15, 0.2], 'n_estimators': [100, 200, 500, 1000, 2000], 'max_depth': [3, 5, 10], 'colsample_bytree': [0.1, 0.3, 0.5, 1], 'subsample': [0.1, 0.3, 0.5, 1]},
pre_dispatch='2*n_jobs', random_state=None, refit=True,
return_train_score=True, scoring=None, verbose=10)
In [0]:
print (random_cfl1.best_params_)
{'subsample': 1, 'n_estimators': 500, 'max_depth': 5, 'learning_rate': 0.05, 'colsample_bytree': 0.5}
In [0]:
# Training a hyper-parameter tuned Xg-Boost regressor on our train data
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
x_cfl=XGBClassifier(n_estimators=2000, learning_rate=0.05, colsample_bytree=1, max_depth=3)
x_cfl.fit(X_train,y_train)
c_cfl=CalibratedClassifierCV(x_cfl,method='sigmoid')
c_cfl.fit(X_train,y_train)
predict_y = c_cfl.predict_proba(X_train)
print ('train loss',log_loss(y_train, predict_y))
predict_y = c_cfl.predict_proba(X_cv)
print ('cv loss',log_loss(y_cv, predict_y))
predict_y = c_cfl.predict_proba(X_test)
print ('test loss',log_loss(y_test, predict_y))
train loss 0.022540976086 cv loss 0.0928710624158 test loss 0.0782688587098
4.2 Modeling with .asm files
There are 10868 files of asm All the files make up about 150 GB The asm files contains : 1. Address 2. Segments 3. Opcodes 4. Registers 5. function calls 6. APIs With the help of parallel processing we extracted all the features.In parallel we can use all the cores that are present in our computer. Here we extracted 52 features from all the asm files which are important. We read the top solutions and handpicked the features from those papers/videos/blogs.
Refer:https://www.kaggle.com/c/malware-classification/discussion
4.2.1 Feature extraction from asm files
In [0]:
#intially create five folders
#first
#second
#thrid
#fourth
#fifth
#this code tells us about random split of files into five folders
folder_1 ='first'
folder_2 ='second'
folder_3 ='third'
folder_4 ='fourth'
folder_5 ='fifth'
folder_6 = 'output'
for i in [folder_1,folder_2,folder_3,folder_4,folder_5,folder_6]:
if not os.path.isdir(i):
os.makedirs(i)
source='train/'
files = os.listdir('train')
ID=df['Id'].tolist()
data=range(0,10868)
r.shuffle(data)
count=0
for i in range(0,10868):
if i % 5==0:
shutil.move(source+files[data[i]],'first')
elif i%5==1:
shutil.move(source+files[data[i]],'second')
elif i%5 ==2:
shutil.move(source+files[data[i]],'thrid')
elif i%5 ==3:
shutil.move(source+files[data[i]],'fourth')
elif i%5==4:
shutil.move(source+files[data[i]],'fifth')
In [0]:
#http://flint.cs.yale.edu/cs421/papers/x86-asm/asm.html
def firstprocess():
#The prefixes tells about the segments that are present in the asm files
#There are 450 segments(approx) present in all asm files.
#this prefixes are best segments that gives us best values.
#https://en.wikipedia.org/wiki/Data_segment
prefixes = ['HEADER:','.text:','.Pav:','.idata:','.data:','.bss:','.rdata:','.edata:','.rsrc:','.tls:','.reloc:','.BSS:','.CODE']
#this are opcodes that are used to get best results
#https://en.wikipedia.org/wiki/X86_instruction_listings
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
#best keywords that are taken from different blogs
keywords = ['.dll','std::',':dword']
#Below taken registers are general purpose registers and special registers
#All the registers which are taken are best
registers=['edx','esi','eax','ebx','ecx','edi','ebp','esp','eip']
file1=open("output\asmsmallfile.txt","w+")
files = os.listdir('first')
for f in files:
#filling the values with zeros into the arrays
prefixescount=np.zeros(len(prefixes),dtype=int)
opcodescount=np.zeros(len(opcodes),dtype=int)
keywordcount=np.zeros(len(keywords),dtype=int)
registerscount=np.zeros(len(registers),dtype=int)
features=[]
f2=f.split('.')[0]
file1.write(f2+",")
opcodefile.write(f2+" ")
# https://docs.python.org/3/library/codecs.html#codecs.ignore_errors
# https://docs.python.org/3/library/codecs.html#codecs.Codec.encode
with codecs.open('first/'+f,encoding='cp1252',errors ='replace') as fli:
for lines in fli:
# https://www.tutorialspoint.com/python3/string_rstrip.htm
line=lines.rstrip().split()
l=line[0]
#counting the prefixs in each and every line
for i in range(len(prefixes)):
if prefixes[i] in line[0]:
prefixescount[i]+=1
line=line[1:]
#counting the opcodes in each and every line
for i in range(len(opcodes)):
if any(opcodes[i]==li for li in line):
features.append(opcodes[i])
opcodescount[i]+=1
#counting registers in the line
for i in range(len(registers)):
for li in line:
# we will use registers only in 'text' and 'CODE' segments
if registers[i] in li and ('text' in l or 'CODE' in l):
registerscount[i]+=1
#counting keywords in the line
for i in range(len(keywords)):
for li in line:
if keywords[i] in li:
keywordcount[i]+=1
#pushing the values into the file after reading whole file
for prefix in prefixescount:
file1.write(str(prefix)+",")
for opcode in opcodescount:
file1.write(str(opcode)+",")
for register in registerscount:
file1.write(str(register)+",")
for key in keywordcount:
file1.write(str(key)+",")
file1.write("\n")
file1.close()
#same as above
def secondprocess():
prefixes = ['HEADER:','.text:','.Pav:','.idata:','.data:','.bss:','.rdata:','.edata:','.rsrc:','.tls:','.reloc:','.BSS:','.CODE']
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
keywords = ['.dll','std::',':dword']
registers=['edx','esi','eax','ebx','ecx','edi','ebp','esp','eip']
file1=open("output\mediumasmfile.txt","w+")
files = os.listdir('second')
for f in files:
prefixescount=np.zeros(len(prefixes),dtype=int)
opcodescount=np.zeros(len(opcodes),dtype=int)
keywordcount=np.zeros(len(keywords),dtype=int)
registerscount=np.zeros(len(registers),dtype=int)
features=[]
f2=f.split('.')[0]
file1.write(f2+",")
opcodefile.write(f2+" ")
with codecs.open('second/'+f,encoding='cp1252',errors ='replace') as fli:
for lines in fli:
line=lines.rstrip().split()
l=line[0]
for i in range(len(prefixes)):
if prefixes[i] in line[0]:
prefixescount[i]+=1
line=line[1:]
for i in range(len(opcodes)):
if any(opcodes[i]==li for li in line):
features.append(opcodes[i])
opcodescount[i]+=1
for i in range(len(registers)):
for li in line:
if registers[i] in li and ('text' in l or 'CODE' in l):
registerscount[i]+=1
for i in range(len(keywords)):
for li in line:
if keywords[i] in li:
keywordcount[i]+=1
for prefix in prefixescount:
file1.write(str(prefix)+",")
for opcode in opcodescount:
file1.write(str(opcode)+",")
for register in registerscount:
file1.write(str(register)+",")
for key in keywordcount:
file1.write(str(key)+",")
file1.write("\n")
file1.close()
# same as smallprocess() functions
def thirdprocess():
prefixes = ['HEADER:','.text:','.Pav:','.idata:','.data:','.bss:','.rdata:','.edata:','.rsrc:','.tls:','.reloc:','.BSS:','.CODE']
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
keywords = ['.dll','std::',':dword']
registers=['edx','esi','eax','ebx','ecx','edi','ebp','esp','eip']
file1=open("output\largeasmfile.txt","w+")
files = os.listdir('thrid')
for f in files:
prefixescount=np.zeros(len(prefixes),dtype=int)
opcodescount=np.zeros(len(opcodes),dtype=int)
keywordcount=np.zeros(len(keywords),dtype=int)
registerscount=np.zeros(len(registers),dtype=int)
features=[]
f2=f.split('.')[0]
file1.write(f2+",")
opcodefile.write(f2+" ")
with codecs.open('thrid/'+f,encoding='cp1252',errors ='replace') as fli:
for lines in fli:
line=lines.rstrip().split()
l=line[0]
for i in range(len(prefixes)):
if prefixes[i] in line[0]:
prefixescount[i]+=1
line=line[1:]
for i in range(len(opcodes)):
if any(opcodes[i]==li for li in line):
features.append(opcodes[i])
opcodescount[i]+=1
for i in range(len(registers)):
for li in line:
if registers[i] in li and ('text' in l or 'CODE' in l):
registerscount[i]+=1
for i in range(len(keywords)):
for li in line:
if keywords[i] in li:
keywordcount[i]+=1
for prefix in prefixescount:
file1.write(str(prefix)+",")
for opcode in opcodescount:
file1.write(str(opcode)+",")
for register in registerscount:
file1.write(str(register)+",")
for key in keywordcount:
file1.write(str(key)+",")
file1.write("\n")
file1.close()
def fourthprocess():
prefixes = ['HEADER:','.text:','.Pav:','.idata:','.data:','.bss:','.rdata:','.edata:','.rsrc:','.tls:','.reloc:','.BSS:','.CODE']
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
keywords = ['.dll','std::',':dword']
registers=['edx','esi','eax','ebx','ecx','edi','ebp','esp','eip']
file1=open("output\hugeasmfile.txt","w+")
files = os.listdir('fourth/')
for f in files:
prefixescount=np.zeros(len(prefixes),dtype=int)
opcodescount=np.zeros(len(opcodes),dtype=int)
keywordcount=np.zeros(len(keywords),dtype=int)
registerscount=np.zeros(len(registers),dtype=int)
features=[]
f2=f.split('.')[0]
file1.write(f2+",")
opcodefile.write(f2+" ")
with codecs.open('fourth/'+f,encoding='cp1252',errors ='replace') as fli:
for lines in fli:
line=lines.rstrip().split()
l=line[0]
for i in range(len(prefixes)):
if prefixes[i] in line[0]:
prefixescount[i]+=1
line=line[1:]
for i in range(len(opcodes)):
if any(opcodes[i]==li for li in line):
features.append(opcodes[i])
opcodescount[i]+=1
for i in range(len(registers)):
for li in line:
if registers[i] in li and ('text' in l or 'CODE' in l):
registerscount[i]+=1
for i in range(len(keywords)):
for li in line:
if keywords[i] in li:
keywordcount[i]+=1
for prefix in prefixescount:
file1.write(str(prefix)+",")
for opcode in opcodescount:
file1.write(str(opcode)+",")
for register in registerscount:
file1.write(str(register)+",")
for key in keywordcount:
file1.write(str(key)+",")
file1.write("\n")
file1.close()
def fifthprocess():
prefixes = ['HEADER:','.text:','.Pav:','.idata:','.data:','.bss:','.rdata:','.edata:','.rsrc:','.tls:','.reloc:','.BSS:','.CODE']
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
keywords = ['.dll','std::',':dword']
registers=['edx','esi','eax','ebx','ecx','edi','ebp','esp','eip']
file1=open("output\trainasmfile.txt","w+")
files = os.listdir('fifth/')
for f in files:
prefixescount=np.zeros(len(prefixes),dtype=int)
opcodescount=np.zeros(len(opcodes),dtype=int)
keywordcount=np.zeros(len(keywords),dtype=int)
registerscount=np.zeros(len(registers),dtype=int)
features=[]
f2=f.split('.')[0]
file1.write(f2+",")
opcodefile.write(f2+" ")
with codecs.open('fifth/'+f,encoding='cp1252',errors ='replace') as fli:
for lines in fli:
line=lines.rstrip().split()
l=line[0]
for i in range(len(prefixes)):
if prefixes[i] in line[0]:
prefixescount[i]+=1
line=line[1:]
for i in range(len(opcodes)):
if any(opcodes[i]==li for li in line):
features.append(opcodes[i])
opcodescount[i]+=1
for i in range(len(registers)):
for li in line:
if registers[i] in li and ('text' in l or 'CODE' in l):
registerscount[i]+=1
for i in range(len(keywords)):
for li in line:
if keywords[i] in li:
keywordcount[i]+=1
for prefix in prefixescount:
file1.write(str(prefix)+",")
for opcode in opcodescount:
file1.write(str(opcode)+",")
for register in registerscount:
file1.write(str(register)+",")
for key in keywordcount:
file1.write(str(key)+",")
file1.write("\n")
file1.close()
def main():
#the below code is used for multiprogramming
#the number of process depends upon the number of cores present System
#process is used to call multiprogramming
manager=multiprocessing.Manager()
p1=Process(target=firstprocess)
p2=Process(target=secondprocess)
p3=Process(target=thirdprocess)
p4=Process(target=fourthprocess)
p5=Process(target=fifthprocess)
#p1.start() is used to start the thread execution
p1.start()
p2.start()
p3.start()
p4.start()
p5.start()
#After completion all the threads are joined
p1.join()
p2.join()
p3.join()
p4.join()
p5.join()
if __name__=="__main__":
main()
In [0]:
# asmoutputfile.csv(output genarated from the above two cells) will contain all the extracted features from .asm files
# this file will be uploaded in the drive, you can directly use this
dfasm=pd.read_csv("asmoutputfile.csv")
Y.columns = ['ID', 'Class']
result_asm = pd.merge(dfasm, Y,on='ID', how='left')
result_asm.head()
Out[0]:
| ID | HEADER: | .text: | .Pav: | .idata: | .data: | .bss: | .rdata: | .edata: | .rsrc: | ... | edx | esi | eax | ebx | ecx | edi | ebp | esp | eip | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01kcPWA9K2BOxQeS5Rju | 19 | 744 | 0 | 127 | 57 | 0 | 323 | 0 | 3 | ... | 18 | 66 | 15 | 43 | 83 | 0 | 17 | 48 | 29 | 1 |
| 1 | 1E93CpP60RHFNiT5Qfvn | 17 | 838 | 0 | 103 | 49 | 0 | 0 | 0 | 3 | ... | 18 | 29 | 48 | 82 | 12 | 0 | 14 | 0 | 20 | 1 |
| 2 | 3ekVow2ajZHbTnBcsDfX | 17 | 427 | 0 | 50 | 43 | 0 | 145 | 0 | 3 | ... | 13 | 42 | 10 | 67 | 14 | 0 | 11 | 0 | 9 | 1 |
| 3 | 3X2nY7iQaPBIWDrAZqJe | 17 | 227 | 0 | 43 | 19 | 0 | 0 | 0 | 3 | ... | 6 | 8 | 14 | 7 | 2 | 0 | 8 | 0 | 6 | 1 |
| 4 | 46OZzdsSKDCFV8h7XWxf | 17 | 402 | 0 | 59 | 170 | 0 | 0 | 0 | 3 | ... | 12 | 9 | 18 | 29 | 5 | 0 | 11 | 0 | 11 | 1 |
5 rows × 53 columns
4.2.1.1 Files sizes of each .asm file
In [0]:
#file sizes of byte files
files=os.listdir('asmFiles')
filenames=Y['ID'].tolist()
class_y=Y['Class'].tolist()
class_bytes=[]
sizebytes=[]
fnames=[]
for file in files:
# print(os.stat('byteFiles/0A32eTdBKayjCWhZqDOQ.txt'))
# os.stat_result(st_mode=33206, st_ino=1125899906874507, st_dev=3561571700, st_nlink=1, st_uid=0, st_gid=0,
# st_size=3680109, st_atime=1519638522, st_mtime=1519638522, st_ctime=1519638522)
# read more about os.stat: here https://www.tutorialspoint.com/python/os_stat.htm
statinfo=os.stat('asmFiles/'+file)
# split the file name at '.' and take the first part of it i.e the file name
file=file.split('.')[0]
if any(file == filename for filename in filenames):
i=filenames.index(file)
class_bytes.append(class_y[i])
# converting into Mb's
sizebytes.append(statinfo.st_size/(1024.0*1024.0))
fnames.append(file)
asm_size_byte=pd.DataFrame({'ID':fnames,'size':sizebytes,'Class':class_bytes})
print (asm_size_byte.head())
Class ID size 0 9 01azqd4InC7m9JpocGv5 56.229886 1 2 01IsoiSMh5gxyDYTl4CB 13.999378 2 9 01jsnpXSAlgw6aPeDxrU 8.507785 3 1 01kcPWA9K2BOxQeS5Rju 0.078190 4 8 01SuzwMJEIXsK7A8dQbl 0.996723
4.2.1.2 Distribution of .asm file sizes
In [0]:
#boxplot of asm files
ax = sns.boxplot(x="Class", y="size", data=asm_size_byte)
plt.title("boxplot of .bytes file sizes")
plt.show()
In [0]:
# add the file size feature to previous extracted features
print(result_asm.shape)
print(asm_size_byte.shape)
result_asm = pd.merge(result_asm, asm_size_byte.drop(['Class'], axis=1),on='ID', how='left')
result_asm.head()
(10868, 53) (10868, 3)
Out[0]:
| ID | HEADER: | .text: | .Pav: | .idata: | .data: | .bss: | .rdata: | .edata: | .rsrc: | ... | esi | eax | ebx | ecx | edi | ebp | esp | eip | Class | size | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01kcPWA9K2BOxQeS5Rju | 19 | 744 | 0 | 127 | 57 | 0 | 323 | 0 | 3 | ... | 66 | 15 | 43 | 83 | 0 | 17 | 48 | 29 | 1 | 0.078190 |
| 1 | 1E93CpP60RHFNiT5Qfvn | 17 | 838 | 0 | 103 | 49 | 0 | 0 | 0 | 3 | ... | 29 | 48 | 82 | 12 | 0 | 14 | 0 | 20 | 1 | 0.063400 |
| 2 | 3ekVow2ajZHbTnBcsDfX | 17 | 427 | 0 | 50 | 43 | 0 | 145 | 0 | 3 | ... | 42 | 10 | 67 | 14 | 0 | 11 | 0 | 9 | 1 | 0.041695 |
| 3 | 3X2nY7iQaPBIWDrAZqJe | 17 | 227 | 0 | 43 | 19 | 0 | 0 | 0 | 3 | ... | 8 | 14 | 7 | 2 | 0 | 8 | 0 | 6 | 1 | 0.018757 |
| 4 | 46OZzdsSKDCFV8h7XWxf | 17 | 402 | 0 | 59 | 170 | 0 | 0 | 0 | 3 | ... | 9 | 18 | 29 | 5 | 0 | 11 | 0 | 11 | 1 | 0.037567 |
5 rows × 54 columns
In [0]:
# we normalize the data each column
result_asm = normalize(result_asm)
result_asm.head()
Out[0]:
| ID | HEADER: | .text: | .Pav: | .idata: | .data: | .bss: | .rdata: | .edata: | .rsrc: | ... | esi | eax | ebx | ecx | edi | ebp | esp | eip | Class | size | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01kcPWA9K2BOxQeS5Rju | 0.107345 | 0.001092 | 0.0 | 0.000761 | 0.000023 | 0.0 | 0.000084 | 0.0 | 0.000072 | ... | 0.000746 | 0.000301 | 0.000360 | 0.001057 | 0.0 | 0.030797 | 0.001468 | 0.003173 | 1 | 0.000432 |
| 1 | 1E93CpP60RHFNiT5Qfvn | 0.096045 | 0.001230 | 0.0 | 0.000617 | 0.000019 | 0.0 | 0.000000 | 0.0 | 0.000072 | ... | 0.000328 | 0.000965 | 0.000686 | 0.000153 | 0.0 | 0.025362 | 0.000000 | 0.002188 | 1 | 0.000327 |
| 2 | 3ekVow2ajZHbTnBcsDfX | 0.096045 | 0.000627 | 0.0 | 0.000300 | 0.000017 | 0.0 | 0.000038 | 0.0 | 0.000072 | ... | 0.000475 | 0.000201 | 0.000560 | 0.000178 | 0.0 | 0.019928 | 0.000000 | 0.000985 | 1 | 0.000172 |
| 3 | 3X2nY7iQaPBIWDrAZqJe | 0.096045 | 0.000333 | 0.0 | 0.000258 | 0.000008 | 0.0 | 0.000000 | 0.0 | 0.000072 | ... | 0.000090 | 0.000281 | 0.000059 | 0.000025 | 0.0 | 0.014493 | 0.000000 | 0.000657 | 1 | 0.000009 |
| 4 | 46OZzdsSKDCFV8h7XWxf | 0.096045 | 0.000590 | 0.0 | 0.000353 | 0.000068 | 0.0 | 0.000000 | 0.0 | 0.000072 | ... | 0.000102 | 0.000362 | 0.000243 | 0.000064 | 0.0 | 0.019928 | 0.000000 | 0.001204 | 1 | 0.000143 |
5 rows × 54 columns
4.2.2 Univariate analysis on asm file features
In [0]:
ax = sns.boxplot(x="Class", y=".text:", data=result_asm)
plt.title("boxplot of .asm text segment")
plt.show()
The plot is between Text and class Class 1,2 and 9 can be easly separated
In [0]:
ax = sns.boxplot(x="Class", y=".Pav:", data=result_asm)
plt.title("boxplot of .asm pav segment")
plt.show()
In [0]:
ax = sns.boxplot(x="Class", y=".data:", data=result_asm)
plt.title("boxplot of .asm data segment")
plt.show()
The plot is between data segment and class label class 6 and class 9 can be easily separated from given points
In [0]:
ax = sns.boxplot(x="Class", y=".bss:", data=result_asm)
plt.title("boxplot of .asm bss segment")
plt.show()
plot between bss segment and class label very less number of files are having bss segment
In [0]:
ax = sns.boxplot(x="Class", y=".rdata:", data=result_asm)
plt.title("boxplot of .asm rdata segment")
plt.show()
Plot between rdata segment and Class segment Class 2 can be easily separated 75 pecentile files are having 1M rdata lines
In [0]:
ax = sns.boxplot(x="Class", y="jmp", data=result_asm)
plt.title("boxplot of .asm jmp opcode")
plt.show()
plot between jmp and Class label Class 1 is having frequency of 2000 approx in 75 perentile of files
In [0]:
ax = sns.boxplot(x="Class", y="mov", data=result_asm)
plt.title("boxplot of .asm mov opcode")
plt.show()
plot between Class label and mov opcode Class 1 is having frequency of 2000 approx in 75 perentile of files
In [0]:
ax = sns.boxplot(x="Class", y="retf", data=result_asm)
plt.title("boxplot of .asm retf opcode")
plt.show()
plot between Class label and retf Class 6 can be easily separated with opcode retf The frequency of retf is approx of 250.
In [0]:
ax = sns.boxplot(x="Class", y="push", data=result_asm)
plt.title("boxplot of .asm push opcode")
plt.show()
plot between push opcode and Class label Class 1 is having 75 precentile files with push opcodes of frequency 1000
4.2.2 Multivariate Analysis on .asm file features
In [0]:
# check out the course content for more explantion on tsne algorithm
# https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/t-distributed-stochastic-neighbourhood-embeddingt-sne-part-1/
#multivariate analysis on byte files
#this is with perplexity 50
xtsne=TSNE(perplexity=50)
results=xtsne.fit_transform(result_asm.drop(['ID','Class'], axis=1).fillna(0))
vis_x = results[:, 0]
vis_y = results[:, 1 ]
plt.scatter(vis_x, vis_y, c=data_y, cmap=plt.cm.get_cmap("jet", 9))
plt.colorbar(ticks=range(10))
plt.clim(0.5, 9)
plt.show()
In [0]:
# by univariate analysis on the .asm file features we are getting very negligible information from
# 'rtn', '.BSS:' '.CODE' features, so heare we are trying multivariate analysis after removing those features
# the plot looks very messy
xtsne=TSNE(perplexity=30)
results=xtsne.fit_transform(result_asm.drop(['ID','Class', 'rtn', '.BSS:', '.CODE','size'], axis=1))
vis_x = results[:, 0]
vis_y = results[:, 1]
plt.scatter(vis_x, vis_y, c=data_y, cmap=plt.cm.get_cmap("jet", 9))
plt.colorbar(ticks=range(10))
plt.clim(0.5, 9)
plt.show()
TSNE for asm data with perplexity 50
4.2.3 Conclusion on EDA
- 1. Class 3 can be easily separated because of the frequency of segments,opcodes and keywords being less
- 2. Each feature has its unique importance in separating the Class labels.
4.3 Train and test split
In [0]:
asm_y = result_asm['Class']
asm_x = result_asm.drop(['ID','Class','.BSS:','rtn','.CODE'], axis=1)
In [0]:
X_train_asm, X_test_asm, y_train_asm, y_test_asm = train_test_split(asm_x,asm_y ,stratify=asm_y,test_size=0.20)
X_train_asm, X_cv_asm, y_train_asm, y_cv_asm = train_test_split(X_train_asm, y_train_asm,stratify=y_train_asm,test_size=0.20)
In [0]:
print( X_cv_asm.isnull().all())
HEADER: False .text: False .Pav: False .idata: False .data: False .bss: False .rdata: False .edata: False .rsrc: False .tls: False .reloc: False jmp False mov False retf False push False pop False xor False retn False nop False sub False inc False dec False add False imul False xchg False or False shr False cmp False call False shl False ror False rol False jnb False jz False lea False movzx False .dll False std:: False :dword False edx False esi False eax False ebx False ecx False edi False ebp False esp False eip False size False dtype: bool
4.4. Machine Learning models on features of .asm files
4.4.1 K-Nearest Neigbors
In [0]:
# find more about KNeighborsClassifier() here http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
# -------------------------
# default parameter
# KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2,
# metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)
# methods of
# fit(X, y) : Fit the model using X as training data and y as target values
# predict(X):Predict the class labels for the provided data
# predict_proba(X):Return probability estimates for the test data X.
#-------------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/k-nearest-neighbors-geometric-intuition-with-a-toy-example-1/
#-------------------------------------
# find more about CalibratedClassifierCV here at http://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html
# ----------------------------
# default paramters
# sklearn.calibration.CalibratedClassifierCV(base_estimator=None, method=’sigmoid’, cv=3)
#
# some of the methods of CalibratedClassifierCV()
# fit(X, y[, sample_weight]) Fit the calibrated model
# get_params([deep]) Get parameters for this estimator.
# predict(X) Predict the target of new samples.
# predict_proba(X) Posterior probabilities of classification
#-------------------------------------
# video link:
#-------------------------------------
alpha = [x for x in range(1, 21,2)]
cv_log_error_array=[]
for i in alpha:
k_cfl=KNeighborsClassifier(n_neighbors=i)
k_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(k_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_cv_asm)
cv_log_error_array.append(log_loss(y_cv_asm, predict_y, labels=k_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for k = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
k_cfl=KNeighborsClassifier(n_neighbors=alpha[best_alpha])
k_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(k_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
pred_y=sig_clf.predict(X_test_asm)
predict_y = sig_clf.predict_proba(X_train_asm)
print ('log loss for train data',log_loss(y_train_asm, predict_y))
predict_y = sig_clf.predict_proba(X_cv_asm)
print ('log loss for cv data',log_loss(y_cv_asm, predict_y))
predict_y = sig_clf.predict_proba(X_test_asm)
print ('log loss for test data',log_loss(y_test_asm, predict_y))
plot_confusion_matrix(y_test_asm,sig_clf.predict(X_test_asm))
log_loss for k = 1 is 0.104531321344 log_loss for k = 3 is 0.0958800580948 log_loss for k = 5 is 0.0995466557335 log_loss for k = 7 is 0.107227274345 log_loss for k = 9 is 0.119239543547 log_loss for k = 11 is 0.133926642781 log_loss for k = 13 is 0.147643793967 log_loss for k = 15 is 0.159439699615 log_loss for k = 17 is 0.16878376444 log_loss for k = 19 is 0.178020728839
log loss for train data 0.0476773462198 log loss for cv data 0.0958800580948 log loss for test data 0.0894810720832 Number of misclassified points 2.02391904324 -------------------------------------------------- Confusion matrix --------------------------------------------------
-------------------------------------------------- Precision matrix --------------------------------------------------
Sum of columns in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.] -------------------------------------------------- Recall matrix --------------------------------------------------
Sum of rows in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.]
4.4.2 Logistic Regression
In [0]:
# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
# ------------------------------
# default parameters
# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None,
# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5,
# class_weight=None, warm_start=False, average=False, n_iter=None)
# some of methods
# fit(X, y[, coef_init, intercept_init, …]) Fit linear model with Stochastic Gradient Descent.
# predict(X) Predict class labels for samples in X.
#-------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/geometric-intuition-1/
#------------------------------
alpha = [10 ** x for x in range(-5, 4)]
cv_log_error_array=[]
for i in alpha:
logisticR=LogisticRegression(penalty='l2',C=i,class_weight='balanced')
logisticR.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(logisticR, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_cv_asm)
cv_log_error_array.append(log_loss(y_cv_asm, predict_y, labels=logisticR.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
logisticR=LogisticRegression(penalty='l2',C=alpha[best_alpha],class_weight='balanced')
logisticR.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(logisticR, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_train_asm)
print ('log loss for train data',(log_loss(y_train_asm, predict_y, labels=logisticR.classes_, eps=1e-15)))
predict_y = sig_clf.predict_proba(X_cv_asm)
print ('log loss for cv data',(log_loss(y_cv_asm, predict_y, labels=logisticR.classes_, eps=1e-15)))
predict_y = sig_clf.predict_proba(X_test_asm)
print ('log loss for test data',(log_loss(y_test_asm, predict_y, labels=logisticR.classes_, eps=1e-15)))
plot_confusion_matrix(y_test_asm,sig_clf.predict(X_test_asm))
log_loss for c = 1e-05 is 1.58867274165 log_loss for c = 0.0001 is 1.54560797884 log_loss for c = 0.001 is 1.30137786807 log_loss for c = 0.01 is 1.33317456931 log_loss for c = 0.1 is 1.16705751378 log_loss for c = 1 is 0.757667807779 log_loss for c = 10 is 0.546533939819 log_loss for c = 100 is 0.438414998062 log_loss for c = 1000 is 0.424423536526
log loss for train data 0.396219394701 log loss for cv data 0.424423536526 log loss for test data 0.415685592517 Number of misclassified points 9.61361545538 -------------------------------------------------- Confusion matrix --------------------------------------------------
-------------------------------------------------- Precision matrix --------------------------------------------------
Sum of columns in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.] -------------------------------------------------- Recall matrix --------------------------------------------------
Sum of rows in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.]
4.4.3 Random Forest Classifier
In [0]:
# --------------------------------
# default parameters
# sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion=’gini’, max_depth=None, min_samples_split=2,
# min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0,
# min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False,
# class_weight=None)
# Some of methods of RandomForestClassifier()
# fit(X, y, [sample_weight]) Fit the SVM model according to the given training data.
# predict(X) Perform classification on samples in X.
# predict_proba (X) Perform classification on samples in X.
# some of attributes of RandomForestClassifier()
# feature_importances_ : array of shape = [n_features]
# The feature importances (the higher, the more important the feature).
# --------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/random-forest-and-their-construction-2/
# --------------------------------
alpha=[10,50,100,500,1000,2000,3000]
cv_log_error_array=[]
for i in alpha:
r_cfl=RandomForestClassifier(n_estimators=i,random_state=42,n_jobs=-1)
r_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_cv_asm)
cv_log_error_array.append(log_loss(y_cv_asm, predict_y, labels=r_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
r_cfl=RandomForestClassifier(n_estimators=alpha[best_alpha],random_state=42,n_jobs=-1)
r_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_train_asm)
print ('log loss for train data',(log_loss(y_train_asm, predict_y, labels=sig_clf.classes_, eps=1e-15)))
predict_y = sig_clf.predict_proba(X_cv_asm)
print ('log loss for cv data',(log_loss(y_cv_asm, predict_y, labels=sig_clf.classes_, eps=1e-15)))
predict_y = sig_clf.predict_proba(X_test_asm)
print ('log loss for test data',(log_loss(y_test_asm, predict_y, labels=sig_clf.classes_, eps=1e-15)))
plot_confusion_matrix(y_test_asm,sig_clf.predict(X_test_asm))
log_loss for c = 10 is 0.0581657906023 log_loss for c = 50 is 0.0515443148419 log_loss for c = 100 is 0.0513084973231 log_loss for c = 500 is 0.0499021761479 log_loss for c = 1000 is 0.0497972474298 log_loss for c = 2000 is 0.0497091690815 log_loss for c = 3000 is 0.0496706817633
log loss for train data 0.0116517052676 log loss for cv data 0.0496706817633 log loss for test data 0.0571239496453 Number of misclassified points 1.14995400184 -------------------------------------------------- Confusion matrix --------------------------------------------------
-------------------------------------------------- Precision matrix --------------------------------------------------
Sum of columns in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.] -------------------------------------------------- Recall matrix --------------------------------------------------
Sum of rows in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.]
4.4.4 XgBoost Classifier
In [0]:
# Training a hyper-parameter tuned Xg-Boost regressor on our train data
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
alpha=[10,50,100,500,1000,2000,3000]
cv_log_error_array=[]
for i in alpha:
x_cfl=XGBClassifier(n_estimators=i,nthread=-1)
x_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_cv_asm)
cv_log_error_array.append(log_loss(y_cv_asm, predict_y, labels=x_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
x_cfl=XGBClassifier(n_estimators=alpha[best_alpha],nthread=-1)
x_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_train_asm)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train_asm, predict_y))
predict_y = sig_clf.predict_proba(X_cv_asm)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv_asm, predict_y))
predict_y = sig_clf.predict_proba(X_test_asm)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test_asm, predict_y))
plot_confusion_matrix(y_test_asm,sig_clf.predict(X_test_asm))
log_loss for c = 10 is 0.104344888454 log_loss for c = 50 is 0.0567190635611 log_loss for c = 100 is 0.056075038646 log_loss for c = 500 is 0.057336051683 log_loss for c = 1000 is 0.0571265109903 log_loss for c = 2000 is 0.057103406781 log_loss for c = 3000 is 0.0567993215778
For values of best alpha = 100 The train log loss is: 0.0117883742574 For values of best alpha = 100 The cross validation log loss is: 0.056075038646 For values of best alpha = 100 The test log loss is: 0.0491647763845 Number of misclassified points 0.873965041398 -------------------------------------------------- Confusion matrix --------------------------------------------------
-------------------------------------------------- Precision matrix --------------------------------------------------
Sum of columns in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.] -------------------------------------------------- Recall matrix --------------------------------------------------
Sum of rows in precision matrix [ 1. 1. 1. 1. 1. 1. 1. 1. 1.]
4.4.5 Xgboost Classifier with best hyperparameters
In [0]:
x_cfl=XGBClassifier()
prams={
'learning_rate':[0.01,0.03,0.05,0.1,0.15,0.2],
'n_estimators':[100,200,500,1000,2000],
'max_depth':[3,5,10],
'colsample_bytree':[0.1,0.3,0.5,1],
'subsample':[0.1,0.3,0.5,1]
}
random_cfl=RandomizedSearchCV(x_cfl,param_distributions=prams,verbose=10,n_jobs=-1,)
random_cfl.fit(X_train_asm,y_train_asm)
Fitting 3 folds for each of 10 candidates, totalling 30 fits
[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 8.1s [Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 32.8s [Parallel(n_jobs=-1)]: Done 19 out of 30 | elapsed: 1.1min remaining: 39.3s [Parallel(n_jobs=-1)]: Done 23 out of 30 | elapsed: 1.3min remaining: 23.0s [Parallel(n_jobs=-1)]: Done 27 out of 30 | elapsed: 1.4min remaining: 9.2s [Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 2.3min finished
Out[0]:
RandomizedSearchCV(cv=None, error_score='raise',
estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,
gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,
min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
objective='binary:logistic', reg_alpha=0, reg_lambda=1,
scale_pos_weight=1, seed=0, silent=True, subsample=1),
fit_params=None, iid=True, n_iter=10, n_jobs=-1,
param_distributions={'learning_rate': [0.01, 0.03, 0.05, 0.1, 0.15, 0.2], 'n_estimators': [100, 200, 500, 1000, 2000], 'max_depth': [3, 5, 10], 'colsample_bytree': [0.1, 0.3, 0.5, 1], 'subsample': [0.1, 0.3, 0.5, 1]},
pre_dispatch='2*n_jobs', random_state=None, refit=True,
return_train_score=True, scoring=None, verbose=10)
In [0]:
print (random_cfl.best_params_)
{'subsample': 1, 'n_estimators': 200, 'max_depth': 5, 'learning_rate': 0.15, 'colsample_bytree': 0.5}
In [0]:
# Training a hyper-parameter tuned Xg-Boost regressor on our train data
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
x_cfl=XGBClassifier(n_estimators=200,subsample=0.5,learning_rate=0.15,colsample_bytree=0.5,max_depth=3)
x_cfl.fit(X_train_asm,y_train_asm)
c_cfl=CalibratedClassifierCV(x_cfl,method='sigmoid')
c_cfl.fit(X_train_asm,y_train_asm)
predict_y = c_cfl.predict_proba(X_train_asm)
print ('train loss',log_loss(y_train_asm, predict_y))
predict_y = c_cfl.predict_proba(X_cv_asm)
print ('cv loss',log_loss(y_cv_asm, predict_y))
predict_y = c_cfl.predict_proba(X_test_asm)
print ('test loss',log_loss(y_test_asm, predict_y))
train loss 0.0102661325822 cv loss 0.0501201796687 test loss 0.0483908764397
4.5. Machine Learning models on features of both .asm and .bytes files
4.5.1. Merging both asm and byte file features
In [0]:
result.head()
Out[0]:
| ID | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ... | f9 | fa | fb | fc | fd | fe | ff | ?? | Class | size | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 0.262806 | 0.005498 | 0.001567 | 0.002067 | 0.002048 | 0.001835 | 0.002058 | 0.002946 | 0.002638 | ... | 0.013560 | 0.013107 | 0.013634 | 0.031724 | 0.014549 | 0.014348 | 0.007843 | 0.000129 | 9 | 0.092219 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 0.017358 | 0.011737 | 0.004033 | 0.003876 | 0.005303 | 0.003873 | 0.004747 | 0.006984 | 0.008267 | ... | 0.001920 | 0.001147 | 0.001329 | 0.087867 | 0.002432 | 0.088411 | 0.074851 | 0.000606 | 2 | 0.121236 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 0.040827 | 0.013434 | 0.001429 | 0.001315 | 0.005464 | 0.005280 | 0.005078 | 0.002155 | 0.008104 | ... | 0.009804 | 0.011777 | 0.012604 | 0.028423 | 0.013080 | 0.013937 | 0.067001 | 0.000033 | 9 | 0.084499 |
| 3 | 01kcPWA9K2BOxQeS5Rju | 0.009209 | 0.001708 | 0.000404 | 0.000441 | 0.000770 | 0.000354 | 0.000310 | 0.000481 | 0.000959 | ... | 0.002121 | 0.001886 | 0.002272 | 0.013032 | 0.002211 | 0.003957 | 0.010904 | 0.000984 | 1 | 0.010759 |
| 4 | 01SuzwMJEIXsK7A8dQbl | 0.008629 | 0.001000 | 0.000168 | 0.000234 | 0.000342 | 0.000232 | 0.000148 | 0.000229 | 0.000376 | ... | 0.001530 | 0.000853 | 0.001052 | 0.007511 | 0.001038 | 0.001258 | 0.002998 | 0.000636 | 8 | 0.006233 |
5 rows × 260 columns
In [0]:
result_asm.head()
Out[0]:
| ID | HEADER: | .text: | .Pav: | .idata: | .data: | .bss: | .rdata: | .edata: | .rsrc: | ... | esi | eax | ebx | ecx | edi | ebp | esp | eip | Class | size | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01kcPWA9K2BOxQeS5Rju | 0.107345 | 0.001092 | 0.0 | 0.000761 | 0.000023 | 0.0 | 0.000084 | 0.0 | 0.000072 | ... | 0.000746 | 0.000301 | 0.000360 | 0.001057 | 0.0 | 0.030797 | 0.001468 | 0.003173 | 1 | 0.000432 |
| 1 | 1E93CpP60RHFNiT5Qfvn | 0.096045 | 0.001230 | 0.0 | 0.000617 | 0.000019 | 0.0 | 0.000000 | 0.0 | 0.000072 | ... | 0.000328 | 0.000965 | 0.000686 | 0.000153 | 0.0 | 0.025362 | 0.000000 | 0.002188 | 1 | 0.000327 |
| 2 | 3ekVow2ajZHbTnBcsDfX | 0.096045 | 0.000627 | 0.0 | 0.000300 | 0.000017 | 0.0 | 0.000038 | 0.0 | 0.000072 | ... | 0.000475 | 0.000201 | 0.000560 | 0.000178 | 0.0 | 0.019928 | 0.000000 | 0.000985 | 1 | 0.000172 |
| 3 | 3X2nY7iQaPBIWDrAZqJe | 0.096045 | 0.000333 | 0.0 | 0.000258 | 0.000008 | 0.0 | 0.000000 | 0.0 | 0.000072 | ... | 0.000090 | 0.000281 | 0.000059 | 0.000025 | 0.0 | 0.014493 | 0.000000 | 0.000657 | 1 | 0.000009 |
| 4 | 46OZzdsSKDCFV8h7XWxf | 0.096045 | 0.000590 | 0.0 | 0.000353 | 0.000068 | 0.0 | 0.000000 | 0.0 | 0.000072 | ... | 0.000102 | 0.000362 | 0.000243 | 0.000064 | 0.0 | 0.019928 | 0.000000 | 0.001204 | 1 | 0.000143 |
5 rows × 54 columns
In [0]:
print(result.shape)
print(result_asm.shape)
(10868, 260) (10868, 54)
In [0]:
result_x = pd.merge(result,result_asm.drop(['Class'], axis=1),on='ID', how='left')
result_y = result_x['Class']
result_x = result_x.drop(['ID','rtn','.BSS:','.CODE','Class'], axis=1)
result_x.head()
Out[0]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | edx | esi | eax | ebx | ecx | edi | ebp | esp | eip | size_y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.262806 | 0.005498 | 0.001567 | 0.002067 | 0.002048 | 0.001835 | 0.002058 | 0.002946 | 0.002638 | 0.003531 | ... | 0.015418 | 0.025875 | 0.025744 | 0.004910 | 0.008930 | 0.0 | 0.027174 | 0.000428 | 0.049896 | 0.400910 |
| 1 | 0.017358 | 0.011737 | 0.004033 | 0.003876 | 0.005303 | 0.003873 | 0.004747 | 0.006984 | 0.008267 | 0.000394 | ... | 0.004961 | 0.012316 | 0.007858 | 0.007570 | 0.005350 | 0.0 | 0.043478 | 0.000673 | 0.024839 | 0.099719 |
| 2 | 0.040827 | 0.013434 | 0.001429 | 0.001315 | 0.005464 | 0.005280 | 0.005078 | 0.002155 | 0.008104 | 0.002707 | ... | 0.000095 | 0.006181 | 0.000100 | 0.003773 | 0.000713 | 0.0 | 0.048913 | 0.000000 | 0.012802 | 0.060553 |
| 3 | 0.009209 | 0.001708 | 0.000404 | 0.000441 | 0.000770 | 0.000354 | 0.000310 | 0.000481 | 0.000959 | 0.000521 | ... | 0.000343 | 0.000746 | 0.000301 | 0.000360 | 0.001057 | 0.0 | 0.030797 | 0.001468 | 0.003173 | 0.000432 |
| 4 | 0.008629 | 0.001000 | 0.000168 | 0.000234 | 0.000342 | 0.000232 | 0.000148 | 0.000229 | 0.000376 | 0.000246 | ... | 0.000343 | 0.013875 | 0.000482 | 0.012932 | 0.001363 | 0.0 | 0.027174 | 0.000000 | 0.008316 | 0.006983 |
5 rows × 307 columns
4.5.2. Multivariate Analysis on final fearures
In [0]:
xtsne=TSNE(perplexity=50)
results=xtsne.fit_transform(result_x, axis=1))
vis_x = results[:, 0]
vis_y = results[:, 1]
plt.scatter(vis_x, vis_y, c=result_y, cmap=plt.cm.get_cmap("jet", 9))
plt.colorbar(ticks=range(9))
plt.clim(0.5, 9)
plt.show()
4.5.3. Train and Test split
In [0]:
X_train, X_test_merge, y_train, y_test_merge = train_test_split(result_x, result_y,stratify=result_y,test_size=0.20)
X_train_merge, X_cv_merge, y_train_merge, y_cv_merge = train_test_split(X_train, y_train,stratify=y_train,test_size=0.20)
4.5.4. Random Forest Classifier on final features
In [0]:
# --------------------------------
# default parameters
# sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion=’gini’, max_depth=None, min_samples_split=2,
# min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0,
# min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False,
# class_weight=None)
# Some of methods of RandomForestClassifier()
# fit(X, y, [sample_weight]) Fit the SVM model according to the given training data.
# predict(X) Perform classification on samples in X.
# predict_proba (X) Perform classification on samples in X.
# some of attributes of RandomForestClassifier()
# feature_importances_ : array of shape = [n_features]
# The feature importances (the higher, the more important the feature).
# --------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/random-forest-and-their-construction-2/
# --------------------------------
alpha=[10,50,100,500,1000,2000,3000]
cv_log_error_array=[]
from sklearn.ensemble import RandomForestClassifier
for i in alpha:
r_cfl=RandomForestClassifier(n_estimators=i,random_state=42,n_jobs=-1)
r_cfl.fit(X_train_merge,y_train_merge)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train_merge, y_train_merge)
predict_y = sig_clf.predict_proba(X_cv_merge)
cv_log_error_array.append(log_loss(y_cv_merge, predict_y, labels=r_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
r_cfl=RandomForestClassifier(n_estimators=alpha[best_alpha],random_state=42,n_jobs=-1)
r_cfl.fit(X_train_merge,y_train_merge)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train_merge, y_train_merge)
predict_y = sig_clf.predict_proba(X_train_merge)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train_merge, predict_y))
predict_y = sig_clf.predict_proba(X_cv_merge)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv_merge, predict_y))
predict_y = sig_clf.predict_proba(X_test_merge)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test_merge, predict_y))
log_loss for c = 10 is 0.0461221662017 log_loss for c = 50 is 0.0375229563452 log_loss for c = 100 is 0.0359765822455 log_loss for c = 500 is 0.0358291883873 log_loss for c = 1000 is 0.0358403093496 log_loss for c = 2000 is 0.0357908022178 log_loss for c = 3000 is 0.0355909487962
For values of best alpha = 3000 The train log loss is: 0.0166267614753 For values of best alpha = 3000 The cross validation log loss is: 0.0355909487962 For values of best alpha = 3000 The test log loss is: 0.0401141303589
4.5.5. XgBoost Classifier on final features
In [0]:
# Training a hyper-parameter tuned Xg-Boost regressor on our train data
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
alpha=[10,50,100,500,1000,2000,3000]
cv_log_error_array=[]
for i in alpha:
x_cfl=XGBClassifier(n_estimators=i)
x_cfl.fit(X_train_merge,y_train_merge)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train_merge, y_train_merge)
predict_y = sig_clf.predict_proba(X_cv_merge)
cv_log_error_array.append(log_loss(y_cv_merge, predict_y, labels=x_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
x_cfl=XGBClassifier(n_estimators=3000,nthread=-1)
x_cfl.fit(X_train_merge,y_train_merge,verbose=True)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train_merge, y_train_merge)
predict_y = sig_clf.predict_proba(X_train_merge)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train_merge, predict_y))
predict_y = sig_clf.predict_proba(X_cv_merge)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv_merge, predict_y))
predict_y = sig_clf.predict_proba(X_test_merge)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test_merge, predict_y))
log_loss for c = 10 is 0.0898979446265 log_loss for c = 50 is 0.0536946658041 log_loss for c = 100 is 0.0387968186177 log_loss for c = 500 is 0.0347960327293 log_loss for c = 1000 is 0.0334668083237 log_loss for c = 2000 is 0.0316569078846 log_loss for c = 3000 is 0.0315972694477
For values of best alpha = 3000 The train log loss is: 0.0111918809342 For values of best alpha = 3000 The cross validation log loss is: 0.0315972694477 For values of best alpha = 3000 The test log loss is: 0.0323978515915
4.5.5. XgBoost Classifier on final features with best hyper parameters using Random search
In [0]:
x_cfl=XGBClassifier()
prams={
'learning_rate':[0.01,0.03,0.05,0.1,0.15,0.2],
'n_estimators':[100,200,500,1000,2000],
'max_depth':[3,5,10],
'colsample_bytree':[0.1,0.3,0.5,1],
'subsample':[0.1,0.3,0.5,1]
}
random_cfl=RandomizedSearchCV(x_cfl,param_distributions=prams,verbose=10,n_jobs=-1,)
random_cfl.fit(X_train_merge, y_train_merge)
Fitting 3 folds for each of 10 candidates, totalling 30 fits
[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 1.1min [Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 2.2min [Parallel(n_jobs=-1)]: Done 19 out of 30 | elapsed: 4.5min remaining: 2.6min [Parallel(n_jobs=-1)]: Done 23 out of 30 | elapsed: 5.8min remaining: 1.8min [Parallel(n_jobs=-1)]: Done 27 out of 30 | elapsed: 6.7min remaining: 44.5s [Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 7.4min finished
Out[0]:
RandomizedSearchCV(cv=None, error_score='raise',
estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,
gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,
min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
objective='binary:logistic', reg_alpha=0, reg_lambda=1,
scale_pos_weight=1, seed=0, silent=True, subsample=1),
fit_params=None, iid=True, n_iter=10, n_jobs=-1,
param_distributions={'learning_rate': [0.01, 0.03, 0.05, 0.1, 0.15, 0.2], 'n_estimators': [100, 200, 500, 1000, 2000], 'max_depth': [3, 5, 10], 'colsample_bytree': [0.1, 0.3, 0.5, 1], 'subsample': [0.1, 0.3, 0.5, 1]},
pre_dispatch='2*n_jobs', random_state=None, refit=True,
return_train_score=True, scoring=None, verbose=10)
In [0]:
print (random_cfl.best_params_)
{'subsample': 1, 'n_estimators': 1000, 'max_depth': 10, 'learning_rate': 0.15, 'colsample_bytree': 0.3}
In [0]:
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
x_cfl=XGBClassifier(n_estimators=1000,max_depth=10,learning_rate=0.15,colsample_bytree=0.3,subsample=1,nthread=-1)
x_cfl.fit(X_train_merge,y_train_merge,verbose=True)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train_merge, y_train_merge)
predict_y = sig_clf.predict_proba(X_train_merge)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train_merge, predict_y))
predict_y = sig_clf.predict_proba(X_cv_merge)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv_merge, predict_y))
predict_y = sig_clf.predict_proba(X_test_merge)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test_merge, predict_y))
plot_confusion_matrix(y_test_asm,sig_clf.predict(X_test_merge))
For values of best alpha = 3000 The train log loss is: 0.0121922832297 For values of best alpha = 3000 The cross validation log loss is: 0.0344955487471 For values of best alpha = 3000 The test log loss is: 0.0317041132442
5. Assignments
- Add bi-grams and n-gram features on byte files and improve the log-loss
- Using the 'dchad' github account (https://github.com/dchad/malware-detection), decrease the logloss to <=0.01
- Watch the video ( https://www.youtube.com/watch?v=VLQTRlLGz5Y ) that was in reference section and implement the image features to improve the logloss
5.1 Creating bi-grams for byte file¶
In [0]:
vocab_for_byte = "00,01,02,03,04,05,06,07,08,09,0a,0b,0c,0d,0e,0f,10,11,12,13,14,15,16,17,18,19,1a,1b,1c,1d,1e,1f,20,21,22,23,24,25,26,27,28,29,2a,2b,2c,2d,2e,2f,30,31,32,33,34,35,36,37,38,39,3a,3b,3c,3d,3e,3f,40,41,42,43,44,45,46,47,48,49,4a,4b,4c,4d,4e,4f,50,51,52,53,54,55,56,57,58,59,5a,5b,5c,5d,5e,5f,60,61,62,63,64,65,66,67,68,69,6a,6b,6c,6d,6e,6f,70,71,72,73,74,75,76,77,78,79,7a,7b,7c,7d,7e,7f,80,81,82,83,84,85,86,87,88,89,8a,8b,8c,8d,8e,8f,90,91,92,93,94,95,96,97,98,99,9a,9b,9c,9d,9e,9f,a0,a1,a2,a3,a4,a5,a6,a7,a8,a9,aa,ab,ac,ad,ae,af,b0,b1,b2,b3,b4,b5,b6,b7,b8,b9,ba,bb,bc,bd,be,bf,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,ca,cb,cc,cd,ce,cf,d0,d1,d2,d3,d4,d5,d6,d7,d8,d9,da,db,dc,dd,de,df,e0,e1,e2,e3,e4,e5,e6,e7,e8,e9,ea,eb,ec,ed,ee,ef,f0,f1,f2,f3,f4,f5,f6,f7,f8,f9,fa,fb,fc,fd,fe,ff,??"
vocab_for_byte = vocab_for_byte.split(",")
byte_bigram_vocab = []
for i in vocab_for_byte:
for j in vocab_for_byte:
byte_bigram_vocab.append(i + ' ' + j)
len(byte_bigram_vocab)
Out[0]:
66049
In [0]:
#removal of addres from byte files
# contents of .byte files
# ----------------
#00401000 56 8D 44 24 08 50 8B F1 E8 1C 1B 00 00 C7 06 08
#-------------------
#we remove the starting address 00401000
#code for removing all byte files and getting all text from byte files into text files for analysis
files = os.listdir('byteFiles')
filenames=[]
array=[]
for file in files:
if(file.endswith("bytes")):
file=file.split('.')[0]
text_file = open('byteFiles/'+file+".txt", 'w+')
with open('byteFiles/'+file+".bytes","r") as fp:
lines=""
for line in fp:
a=line.rstrip().split(" ")[1:]
b=' '.join(a)
b=b+"\n"
text_file.write(b)
fp.close()
os.remove('byteFiles/'+file+".bytes")
text_file.close()
In [0]:
files = os.listdir('byteFiles')
from tqdm import tqdm
from nltk.util import ngrams
#program to convert into bag of words of bytefiles
#this is custom-built bag of words this is bigram bag of words
byte_feature_file=open('result_bigram.csv','w+')
byte_feature_file.write("ID,"+','.join(byte_bigram_vocab))
byte_feature_file.write("\n")
for file in tqdm(files):
byte_feature_file.write(file.split('.')[0]+",")
if(file.endswith("txt")):
with open('byteFiles/'+file,"r") as byte_flie:
whole_file = []
vocab_keys = dict(zip(byte_bigram_vocab[:], [0]*len(byte_bigram_vocab[:])))
for lines in byte_flie:
line=lines.rstrip().split(' ')
whole_file.extend(line)
bi_gram = [' '.join(i) for i in list(ngrams(whole_file, 2))] #converting whole file in bigrams('a b c' =>'a b','b c')
for hex_code in bi_gram:
vocab_keys[hex_code.lower()] += 1
byte_flie.close()
new_rows = [str(vocab_keys[i]) for i in byte_bigram_vocab[:]]
byte_feature_file.write(','.join(new_rows)) #addition of new row to the csv file
byte_feature_file.write("\n")
byte_feature_file.close()
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5.2 Collecting Image Features from asm files¶
In [0]:
import array
from scipy import misc
In [0]:
files = os.listdir("asmFiles")
for file in tqdm(files):
filename = file.split('.')[0]
file_to_open = open("asmFiles/" + filename + ".asm", 'rb')
ln = os.path.getsize("asmFiles/" + filename + ".asm")
width = int(ln ** 0.5)
rem = ln%width
a = array.array('B')
a.fromfile(file_to_open, ln-rem)
file_to_open.close()
g = np.reshape(a, (int(len(a)/width), width))
g = np.uint8(g)
misc.imsave('image_feature/' + filename + '.png',g)
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96%|██████████████████████████████████████████████████████████████████████▉ | 10411/10868 [1:59:13<05:19, 1.43it/s] 96%|██████████████████████████████████████████████████████████████████████▉ | 10414/10868 [1:59:14<03:55, 1.93it/s] 96%|██████████████████████████████████████████████████████████████████████▉ | 10415/10868 [1:59:15<05:06, 1.48it/s] 96%|██████████████████████████████████████████████████████████████████████▉ | 10418/10868 [1:59:15<03:37, 2.07it/s] 96%|██████████████████████████████████████████████████████████████████████▉ | 10420/10868 [1:59:15<02:59, 2.50it/s] 96%|██████████████████████████████████████████████████████████████████████▉ | 10422/10868 [1:59:15<02:12, 3.38it/s] 96%|██████████████████████████████████████████████████████████████████████▉ | 10424/10868 [1:59:17<03:54, 1.90it/s] 96%|██████████████████████████████████████████████████████████████████████▉ | 10425/10868 [1:59:18<03:14, 2.28it/s] 96%|██████████████████████████████████████████████████████████████████████▉ | 10427/10868 [1:59:18<02:31, 2.90it/s] 96%|███████████████████████████████████████████████████████████████████████ | 10428/10868 [1:59:19<03:12, 2.29it/s] 96%|███████████████████████████████████████████████████████████████████████ | 10429/10868 [1:59:19<02:32, 2.87it/s] 96%|███████████████████████████████████████████████████████████████████████ | 10430/10868 [1:59:19<03:20, 2.18it/s] 96%|███████████████████████████████████████████████████████████████████████ | 10431/10868 [1:59:21<05:12, 1.40it/s] 96%|███████████████████████████████████████████████████████████████████████ | 10432/10868 [1:59:21<04:13, 1.72it/s] 96%|███████████████████████████████████████████████████████████████████████ | 10433/10868 [1:59:21<03:12, 2.26it/s] 96%|███████████████████████████████████████████████████████████████████████ | 10436/10868 [1:59:21<02:28, 2.90it/s] 96%|███████████████████████████████████████████████████████████████████████ | 10438/10868 [1:59:22<01:53, 3.80it/s] 96%|███████████████████████████████████████████████████████████████████████ | 10439/10868 [1:59:26<10:59, 1.54s/it] 96%|███████████████████████████████████████████████████████████████████████ | 10441/10868 [1:59:26<08:04, 1.13s/it] 96%|███████████████████████████████████████████████████████████████████████ | 10442/10868 [1:59:27<06:06, 1.16it/s] 96%|███████████████████████████████████████████████████████████████████████ | 10445/10868 [1:59:29<05:42, 1.24it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10446/10868 [1:59:29<04:46, 1.47it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10447/10868 [1:59:30<04:52, 1.44it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10448/10868 [1:59:30<04:07, 1.70it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10450/10868 [1:59:32<05:10, 1.34it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10451/10868 [1:59:33<03:53, 1.78it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10452/10868 [1:59:33<03:29, 1.98it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10454/10868 [1:59:33<02:34, 2.68it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10456/10868 [1:59:33<01:55, 3.58it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10457/10868 [1:59:33<01:51, 3.69it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10459/10868 [1:59:35<02:41, 2.53it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10462/10868 [1:59:36<02:51, 2.37it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10463/10868 [1:59:36<02:14, 3.00it/s] 96%|███████████████████████████████████████████████████████████████████████▏ | 10464/10868 [1:59:37<03:37, 1.86it/s] 96%|███████████████████████████████████████████████████████████████████████▎ | 10467/10868 [1:59:39<03:46, 1.77it/s] 96%|███████████████████████████████████████████████████████████████████████▎ | 10469/10868 [1:59:40<02:56, 2.26it/s] 96%|███████████████████████████████████████████████████████████████████████▎ | 10471/10868 [1:59:42<04:35, 1.44it/s] 96%|███████████████████████████████████████████████████████████████████████▎ | 10472/10868 [1:59:42<03:29, 1.89it/s] 96%|███████████████████████████████████████████████████████████████████████▎ | 10473/10868 [1:59:45<08:02, 1.22s/it] 96%|███████████████████████████████████████████████████████████████████████▎ | 10474/10868 [1:59:46<06:19, 1.04it/s] 96%|███████████████████████████████████████████████████████████████████████▎ | 10475/10868 [1:59:46<05:35, 1.17it/s] 96%|███████████████████████████████████████████████████████████████████████▎ | 10476/10868 [1:59:46<04:26, 1.47it/s] 96%|███████████████████████████████████████████████████████████████████████▎ | 10477/10868 [1:59:47<05:11, 1.26it/s] 96%|███████████████████████████████████████████████████████████████████████▎ | 10479/10868 [1:59:48<03:47, 1.71it/s] 96%|███████████████████████████████████████████████████████████████████████▍ | 10483/10868 [1:59:48<02:43, 2.35it/s] 96%|███████████████████████████████████████████████████████████████████████▍ | 10484/10868 [1:59:48<02:31, 2.53it/s] 96%|███████████████████████████████████████████████████████████████████████▍ | 10486/10868 [1:59:48<01:53, 3.37it/s] 96%|███████████████████████████████████████████████████████████████████████▍ | 10487/10868 [1:59:49<02:01, 3.14it/s] 97%|███████████████████████████████████████████████████████████████████████▍ | 10489/10868 [1:59:49<01:30, 4.19it/s] 97%|███████████████████████████████████████████████████████████████████████▍ | 10491/10868 [1:59:50<02:17, 2.74it/s] 97%|███████████████████████████████████████████████████████████████████████▍ | 10493/10868 [1:59:51<02:23, 2.61it/s] 97%|███████████████████████████████████████████████████████████████████████▍ | 10494/10868 [1:59:51<01:52, 3.33it/s] 97%|███████████████████████████████████████████████████████████████████████▍ | 10495/10868 [1:59:51<01:35, 3.92it/s] 97%|███████████████████████████████████████████████████████████████████████▍ | 10497/10868 [1:59:52<02:02, 3.04it/s] 97%|███████████████████████████████████████████████████████████████████████▍ | 10498/10868 [1:59:54<03:47, 1.63it/s] 97%|███████████████████████████████████████████████████████████████████████▍ | 10500/10868 [1:59:56<04:50, 1.27it/s] 97%|███████████████████████████████████████████████████████████████████████▌ | 10501/10868 [1:59:59<08:17, 1.36s/it] 97%|███████████████████████████████████████████████████████████████████████▌ | 10502/10868 [2:00:01<09:37, 1.58s/it] 97%|███████████████████████████████████████████████████████████████████████▌ | 10504/10868 [2:00:01<06:56, 1.14s/it] 97%|███████████████████████████████████████████████████████████████████████▌ | 10505/10868 [2:00:02<06:34, 1.09s/it] 97%|███████████████████████████████████████████████████████████████████████▌ | 10506/10868 [2:00:04<09:09, 1.52s/it] 97%|███████████████████████████████████████████████████████████████████████▌ | 10507/10868 [2:00:05<06:44, 1.12s/it] 97%|███████████████████████████████████████████████████████████████████████▌ | 10508/10868 [2:00:05<05:08, 1.17it/s] 97%|███████████████████████████████████████████████████████████████████████▌ | 10509/10868 [2:00:06<06:16, 1.05s/it] 97%|███████████████████████████████████████████████████████████████████████▌ | 10510/10868 [2:00:08<08:02, 1.35s/it] 97%|███████████████████████████████████████████████████████████████████████▌ | 10511/10868 [2:00:09<06:29, 1.09s/it] 97%|███████████████████████████████████████████████████████████████████████▌ | 10512/10868 [2:00:09<04:54, 1.21it/s] 97%|███████████████████████████████████████████████████████████████████████▌ | 10514/10868 [2:00:10<03:49, 1.54it/s] 97%|███████████████████████████████████████████████████████████████████████▌ | 10515/10868 [2:00:10<03:55, 1.50it/s] 97%|███████████████████████████████████████████████████████████████████████▌ | 10516/10868 [2:00:10<03:08, 1.87it/s] 97%|███████████████████████████████████████████████████████████████████████▌ | 10518/10868 [2:00:12<03:36, 1.61it/s] 97%|███████████████████████████████████████████████████████████████████████▌ | 10519/10868 [2:00:14<05:04, 1.14it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10520/10868 [2:00:14<03:52, 1.50it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10521/10868 [2:00:15<05:03, 1.14it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10522/10868 [2:00:15<03:54, 1.47it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10525/10868 [2:00:16<02:58, 1.92it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10526/10868 [2:00:17<03:40, 1.55it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10527/10868 [2:00:17<02:56, 1.94it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10530/10868 [2:00:20<03:39, 1.54it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10533/10868 [2:00:20<02:35, 2.15it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10535/10868 [2:00:21<02:58, 1.87it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10536/10868 [2:00:21<02:14, 2.46it/s] 97%|███████████████████████████████████████████████████████████████████████▋ | 10537/10868 [2:00:23<03:32, 1.56it/s] 97%|███████████████████████████████████████████████████████████████████████▊ | 10539/10868 [2:00:23<02:37, 2.09it/s] 97%|███████████████████████████████████████████████████████████████████████▊ | 10541/10868 [2:00:24<02:45, 1.97it/s] 97%|███████████████████████████████████████████████████████████████████████▊ | 10543/10868 [2:00:24<02:00, 2.69it/s] 97%|███████████████████████████████████████████████████████████████████████▊ | 10545/10868 [2:00:25<01:53, 2.85it/s] 97%|███████████████████████████████████████████████████████████████████████▊ | 10546/10868 [2:00:26<02:45, 1.95it/s] 97%|███████████████████████████████████████████████████████████████████████▊ | 10549/10868 [2:00:26<02:04, 2.56it/s] 97%|███████████████████████████████████████████████████████████████████████▊ | 10550/10868 [2:00:29<06:00, 1.13s/it] 97%|███████████████████████████████████████████████████████████████████████▊ | 10553/10868 [2:00:31<05:30, 1.05s/it] 97%|███████████████████████████████████████████████████████████████████████▊ | 10554/10868 [2:00:35<09:01, 1.73s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10556/10868 [2:00:39<09:26, 1.82s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10557/10868 [2:00:40<08:47, 1.70s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10559/10868 [2:00:40<06:20, 1.23s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10560/10868 [2:00:41<05:27, 1.06s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10561/10868 [2:00:41<04:16, 1.20it/s] 97%|███████████████████████████████████████████████████████████████████████▉ | 10563/10868 [2:00:42<03:17, 1.54it/s] 97%|███████████████████████████████████████████████████████████████████████▉ | 10564/10868 [2:00:45<06:35, 1.30s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10565/10868 [2:00:47<08:53, 1.76s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10566/10868 [2:00:49<08:23, 1.67s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10567/10868 [2:00:49<06:38, 1.32s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10568/10868 [2:00:50<05:22, 1.08s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10569/10868 [2:00:51<05:10, 1.04s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10570/10868 [2:00:52<05:19, 1.07s/it] 97%|███████████████████████████████████████████████████████████████████████▉ | 10571/10868 [2:00:53<04:48, 1.03it/s] 97%|███████████████████████████████████████████████████████████████████████▉ | 10572/10868 [2:00:53<03:30, 1.41it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10575/10868 [2:00:53<02:39, 1.84it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10576/10868 [2:00:55<03:42, 1.31it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10577/10868 [2:00:56<04:16, 1.13it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10578/10868 [2:00:56<03:19, 1.45it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10579/10868 [2:00:57<03:30, 1.37it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10581/10868 [2:00:58<03:22, 1.42it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10582/10868 [2:00:58<02:39, 1.80it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10584/10868 [2:01:00<02:49, 1.68it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10585/10868 [2:01:00<02:08, 2.21it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10586/10868 [2:01:00<01:44, 2.71it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10587/10868 [2:01:00<01:38, 2.87it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10588/10868 [2:01:04<06:03, 1.30s/it] 97%|████████████████████████████████████████████████████████████████████████ | 10589/10868 [2:01:04<04:58, 1.07s/it] 97%|████████████████████████████████████████████████████████████████████████ | 10591/10868 [2:01:05<03:35, 1.29it/s] 97%|████████████████████████████████████████████████████████████████████████ | 10592/10868 [2:01:06<03:53, 1.18it/s] 97%|████████████████████████████████████████████████████████████████████████▏ | 10593/10868 [2:01:06<03:23, 1.35it/s] 97%|████████████████████████████████████████████████████████████████████████▏ | 10595/10868 [2:01:06<02:30, 1.81it/s] 98%|████████████████████████████████████████████████████████████████████████▏ | 10597/10868 [2:01:07<02:07, 2.13it/s] 98%|████████████████████████████████████████████████████████████████████████▏ | 10598/10868 [2:01:09<04:37, 1.03s/it] 98%|████████████████████████████████████████████████████████████████████████▏ | 10599/10868 [2:01:10<04:52, 1.09s/it] 98%|████████████████████████████████████████████████████████████████████████▏ | 10600/10868 [2:01:11<03:34, 1.25it/s] 98%|████████████████████████████████████████████████████████████████████████▏ | 10601/10868 [2:01:13<05:47, 1.30s/it] 98%|████████████████████████████████████████████████████████████████████████▏ | 10602/10868 [2:01:13<04:17, 1.03it/s] 98%|████████████████████████████████████████████████████████████████████████▏ | 10605/10868 [2:01:14<03:11, 1.37it/s] 98%|████████████████████████████████████████████████████████████████████████▏ | 10606/10868 [2:01:14<03:02, 1.44it/s] 98%|████████████████████████████████████████████████████████████████████████▏ | 10607/10868 [2:01:15<02:43, 1.59it/s] 98%|████████████████████████████████████████████████████████████████████████▏ | 10608/10868 [2:01:15<02:08, 2.02it/s] 98%|████████████████████████████████████████████████████████████████████████▏ | 10609/10868 [2:01:16<03:08, 1.38it/s] 98%|████████████████████████████████████████████████████████████████████████▏ | 10610/10868 [2:01:16<02:30, 1.72it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10612/10868 [2:01:17<02:03, 2.07it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10613/10868 [2:01:18<03:15, 1.31it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10614/10868 [2:01:20<04:00, 1.05it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10615/10868 [2:01:20<03:04, 1.37it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10616/10868 [2:01:20<02:17, 1.84it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10617/10868 [2:01:21<02:09, 1.94it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10618/10868 [2:01:21<01:39, 2.52it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10619/10868 [2:01:22<02:15, 1.84it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10620/10868 [2:01:23<02:47, 1.48it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10621/10868 [2:01:23<02:33, 1.61it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10623/10868 [2:01:23<01:52, 2.19it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10624/10868 [2:01:26<04:34, 1.12s/it] 98%|████████████████████████████████████████████████████████████████████████▎ | 10625/10868 [2:01:26<03:43, 1.09it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10626/10868 [2:01:27<02:54, 1.39it/s] 98%|████████████████████████████████████████████████████████████████████████▎ | 10628/10868 [2:01:27<02:05, 1.92it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10631/10868 [2:01:27<01:38, 2.41it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10632/10868 [2:01:29<02:51, 1.37it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10633/10868 [2:01:29<02:15, 1.73it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10634/10868 [2:01:29<01:44, 2.25it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10636/10868 [2:01:29<01:18, 2.94it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10637/10868 [2:01:31<02:34, 1.50it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10638/10868 [2:01:31<02:28, 1.55it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10639/10868 [2:01:31<01:59, 1.91it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10640/10868 [2:01:32<02:01, 1.88it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10642/10868 [2:01:34<02:17, 1.64it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10643/10868 [2:01:36<04:19, 1.15s/it] 98%|████████████████████████████████████████████████████████████████████████▍ | 10644/10868 [2:01:37<04:18, 1.15s/it] 98%|████████████████████████████████████████████████████████████████████████▍ | 10645/10868 [2:01:37<03:15, 1.14it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10646/10868 [2:01:38<02:35, 1.43it/s] 98%|████████████████████████████████████████████████████████████████████████▍ | 10647/10868 [2:01:38<01:57, 1.89it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10648/10868 [2:01:38<01:46, 2.07it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10650/10868 [2:01:40<02:01, 1.80it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10651/10868 [2:01:40<01:51, 1.94it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10652/10868 [2:01:41<02:28, 1.45it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10654/10868 [2:01:41<01:49, 1.95it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10656/10868 [2:01:42<01:21, 2.61it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10657/10868 [2:01:42<01:13, 2.86it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10658/10868 [2:01:43<02:07, 1.65it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10659/10868 [2:01:46<04:46, 1.37s/it] 98%|████████████████████████████████████████████████████████████████████████▌ | 10660/10868 [2:01:47<04:38, 1.34s/it] 98%|████████████████████████████████████████████████████████████████████████▌ | 10661/10868 [2:01:48<03:28, 1.01s/it] 98%|████████████████████████████████████████████████████████████████████████▌ | 10662/10868 [2:01:49<03:59, 1.16s/it] 98%|████████████████████████████████████████████████████████████████████████▌ | 10663/10868 [2:01:50<03:30, 1.03s/it] 98%|████████████████████████████████████████████████████████████████████████▌ | 10664/10868 [2:01:50<02:51, 1.19it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10665/10868 [2:01:51<02:49, 1.19it/s] 98%|████████████████████████████████████████████████████████████████████████▌ | 10666/10868 [2:01:52<02:24, 1.40it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10667/10868 [2:01:52<01:51, 1.80it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10668/10868 [2:01:52<01:48, 1.85it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10669/10868 [2:01:53<01:41, 1.97it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10670/10868 [2:01:53<01:39, 2.00it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10671/10868 [2:01:53<01:25, 2.31it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10672/10868 [2:01:56<03:03, 1.07it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10673/10868 [2:01:56<02:14, 1.45it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10675/10868 [2:01:59<02:57, 1.09it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10676/10868 [2:01:59<02:13, 1.44it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10677/10868 [2:02:00<03:02, 1.05it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10678/10868 [2:02:01<02:47, 1.13it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10679/10868 [2:02:01<02:21, 1.33it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10680/10868 [2:02:02<01:47, 1.74it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10681/10868 [2:02:02<01:20, 2.31it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10682/10868 [2:02:02<01:20, 2.31it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10683/10868 [2:02:03<01:20, 2.29it/s] 98%|████████████████████████████████████████████████████████████████████████▋ | 10684/10868 [2:02:03<01:04, 2.87it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10685/10868 [2:02:03<00:50, 3.59it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10687/10868 [2:02:05<01:34, 1.92it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10688/10868 [2:02:05<01:11, 2.53it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10690/10868 [2:02:06<01:15, 2.35it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10691/10868 [2:02:08<02:38, 1.11it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10692/10868 [2:02:11<04:02, 1.38s/it] 98%|████████████████████████████████████████████████████████████████████████▊ | 10693/10868 [2:02:12<04:02, 1.39s/it] 98%|████████████████████████████████████████████████████████████████████████▊ | 10694/10868 [2:02:13<03:20, 1.15s/it] 98%|████████████████████████████████████████████████████████████████████████▊ | 10696/10868 [2:02:13<02:22, 1.20it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10697/10868 [2:02:13<01:49, 1.57it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10698/10868 [2:02:13<01:36, 1.76it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10699/10868 [2:02:14<01:17, 2.18it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10700/10868 [2:02:15<02:10, 1.29it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10701/10868 [2:02:16<02:25, 1.15it/s] 98%|████████████████████████████████████████████████████████████████████████▊ | 10702/10868 [2:02:17<02:43, 1.02it/s] 98%|████████████████████████████████████████████████████████████████████████▉ | 10703/10868 [2:02:18<02:05, 1.32it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10705/10868 [2:02:20<02:13, 1.22it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10706/10868 [2:02:23<04:22, 1.62s/it] 99%|████████████████████████████████████████████████████████████████████████▉ | 10707/10868 [2:02:25<04:15, 1.59s/it] 99%|████████████████████████████████████████████████████████████████████████▉ | 10708/10868 [2:02:25<03:09, 1.19s/it] 99%|████████████████████████████████████████████████████████████████████████▉ | 10709/10868 [2:02:25<02:22, 1.11it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10710/10868 [2:02:28<03:53, 1.48s/it] 99%|████████████████████████████████████████████████████████████████████████▉ | 10711/10868 [2:02:28<02:48, 1.07s/it] 99%|████████████████████████████████████████████████████████████████████████▉ | 10712/10868 [2:02:28<02:02, 1.28it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10713/10868 [2:02:30<02:32, 1.01it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10714/10868 [2:02:30<02:24, 1.06it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10715/10868 [2:02:31<01:46, 1.43it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10716/10868 [2:02:32<01:58, 1.28it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10717/10868 [2:02:32<01:30, 1.68it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10719/10868 [2:02:32<01:12, 2.06it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10720/10868 [2:02:33<01:20, 1.85it/s] 99%|████████████████████████████████████████████████████████████████████████▉ | 10721/10868 [2:02:34<01:31, 1.60it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10722/10868 [2:02:34<01:10, 2.07it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10724/10868 [2:02:34<00:57, 2.52it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10725/10868 [2:02:34<00:49, 2.88it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10726/10868 [2:02:35<00:39, 3.61it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10727/10868 [2:02:37<02:01, 1.16it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10728/10868 [2:02:38<02:21, 1.01s/it] 99%|█████████████████████████████████████████████████████████████████████████ | 10729/10868 [2:02:40<02:40, 1.16s/it] 99%|█████████████████████████████████████████████████████████████████████████ | 10730/10868 [2:02:40<01:57, 1.17it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10731/10868 [2:02:40<01:29, 1.53it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10732/10868 [2:02:41<01:53, 1.20it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10733/10868 [2:02:42<01:31, 1.47it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10734/10868 [2:02:42<01:09, 1.94it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10736/10868 [2:02:43<01:06, 1.99it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10737/10868 [2:02:43<01:01, 2.12it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10738/10868 [2:02:44<01:01, 2.10it/s] 99%|█████████████████████████████████████████████████████████████████████████ | 10739/10868 [2:02:45<01:25, 1.50it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10740/10868 [2:02:46<01:33, 1.37it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10741/10868 [2:02:46<01:11, 1.77it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10742/10868 [2:02:46<01:09, 1.82it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10744/10868 [2:02:48<01:15, 1.64it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10745/10868 [2:02:48<00:58, 2.11it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10746/10868 [2:02:48<00:58, 2.10it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10747/10868 [2:02:49<00:58, 2.07it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10748/10868 [2:02:50<01:20, 1.49it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10750/10868 [2:02:50<00:57, 2.05it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10751/10868 [2:02:51<01:13, 1.59it/s] 99%|█████████████████████████████████████████████████████████████████████████▏| 10752/10868 [2:02:53<02:10, 1.12s/it] 99%|█████████████████████████████████████████████████████████████████████████▏| 10753/10868 [2:02:56<02:57, 1.54s/it] 99%|█████████████████████████████████████████████████████████████████████████▏| 10754/10868 [2:02:56<02:19, 1.22s/it] 99%|█████████████████████████████████████████████████████████████████████████▏| 10755/10868 [2:02:59<02:52, 1.52s/it] 99%|█████████████████████████████████████████████████████████████████████████▏| 10756/10868 [2:02:59<02:05, 1.12s/it] 99%|█████████████████████████████████████████████████████████████████████████▏| 10757/10868 [2:02:59<01:31, 1.22it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10758/10868 [2:02:59<01:06, 1.65it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10759/10868 [2:03:00<01:14, 1.47it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10760/10868 [2:03:01<01:21, 1.32it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10761/10868 [2:03:01<01:01, 1.74it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10762/10868 [2:03:02<01:24, 1.26it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10763/10868 [2:03:03<01:25, 1.23it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10764/10868 [2:03:04<01:13, 1.41it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10765/10868 [2:03:05<01:33, 1.10it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10766/10868 [2:03:06<01:34, 1.07it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10767/10868 [2:03:07<01:44, 1.04s/it] 99%|█████████████████████████████████████████████████████████████████████████▎| 10768/10868 [2:03:09<02:03, 1.23s/it] 99%|█████████████████████████████████████████████████████████████████████████▎| 10769/10868 [2:03:10<02:07, 1.29s/it] 99%|█████████████████████████████████████████████████████████████████████████▎| 10770/10868 [2:03:10<01:35, 1.03it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10771/10868 [2:03:11<01:09, 1.40it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10773/10868 [2:03:11<00:53, 1.79it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10775/10868 [2:03:13<00:58, 1.59it/s] 99%|█████████████████████████████████████████████████████████████████████████▎| 10776/10868 [2:03:13<00:43, 2.11it/s] 99%|█████████████████████████████████████████████████████████████████████████▍| 10777/10868 [2:03:13<00:33, 2.69it/s] 99%|█████████████████████████████████████████████████████████████████████████▍| 10778/10868 [2:03:14<00:57, 1.55it/s] 99%|█████████████████████████████████████████████████████████████████████████▍| 10779/10868 [2:03:15<00:53, 1.67it/s] 99%|█████████████████████████████████████████████████████████████████████████▍| 10780/10868 [2:03:15<00:47, 1.85it/s] 99%|█████████████████████████████████████████████████████████████████████████▍| 10783/10868 [2:03:15<00:33, 2.54it/s] 99%|█████████████████████████████████████████████████████████████████████████▍| 10784/10868 [2:03:17<01:00, 1.39it/s] 99%|█████████████████████████████████████████████████████████████████████████▍| 10785/10868 [2:03:17<00:48, 1.70it/s] 99%|█████████████████████████████████████████████████████████████████████████▍| 10786/10868 [2:03:18<01:07, 1.22it/s] 99%|█████████████████████████████████████████████████████████████████████████▍| 10788/10868 [2:03:20<01:05, 1.23it/s] 99%|█████████████████████████████████████████████████████████████████████████▍| 10789/10868 [2:03:25<02:37, 1.99s/it] 99%|█████████████████████████████████████████████████████████████████████████▍| 10790/10868 [2:03:27<02:50, 2.19s/it] 99%|█████████████████████████████████████████████████████████████████████████▍| 10792/10868 [2:03:29<02:13, 1.76s/it] 99%|█████████████████████████████████████████████████████████████████████████▍| 10793/10868 [2:03:30<02:07, 1.70s/it] 99%|█████████████████████████████████████████████████████████████████████████▍| 10794/10868 [2:03:31<01:31, 1.23s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10795/10868 [2:03:32<01:32, 1.27s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10796/10868 [2:03:33<01:33, 1.30s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10797/10868 [2:03:34<01:15, 1.06s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10798/10868 [2:03:34<00:57, 1.22it/s] 99%|█████████████████████████████████████████████████████████████████████████▌| 10799/10868 [2:03:35<01:01, 1.12it/s] 99%|█████████████████████████████████████████████████████████████████████████▌| 10800/10868 [2:03:36<00:55, 1.23it/s] 99%|█████████████████████████████████████████████████████████████████████████▌| 10802/10868 [2:03:37<00:52, 1.25it/s] 99%|█████████████████████████████████████████████████████████████████████████▌| 10803/10868 [2:03:42<02:12, 2.04s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10804/10868 [2:03:43<01:41, 1.59s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10805/10868 [2:03:46<02:05, 1.99s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10806/10868 [2:03:47<01:54, 1.85s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10807/10868 [2:03:51<02:23, 2.35s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10809/10868 [2:03:51<01:39, 1.68s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10810/10868 [2:03:51<01:12, 1.26s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10811/10868 [2:03:52<01:08, 1.20s/it] 99%|█████████████████████████████████████████████████████████████████████████▌| 10812/10868 [2:03:55<01:27, 1.56s/it] 99%|█████████████████████████████████████████████████████████████████████████▋| 10813/10868 [2:03:56<01:15, 1.37s/it] 100%|█████████████████████████████████████████████████████████████████████████▋| 10814/10868 [2:03:56<00:56, 1.04s/it] 100%|█████████████████████████████████████████████████████████████████████████▋| 10815/10868 [2:04:01<02:08, 2.43s/it] 100%|█████████████████████████████████████████████████████████████████████████▋| 10816/10868 [2:04:04<02:03, 2.37s/it] 100%|█████████████████████████████████████████████████████████████████████████▋| 10817/10868 [2:04:04<01:32, 1.81s/it] 100%|█████████████████████████████████████████████████████████████████████████▋| 10818/10868 [2:04:05<01:08, 1.38s/it] 100%|█████████████████████████████████████████████████████████████████████████▋| 10819/10868 [2:04:05<00:50, 1.03s/it] 100%|█████████████████████████████████████████████████████████████████████████▋| 10820/10868 [2:04:07<00:59, 1.24s/it] 100%|█████████████████████████████████████████████████████████████████████████▋| 10821/10868 [2:04:09<01:21, 1.73s/it] 100%|█████████████████████████████████████████████████████████████████████████▋| 10822/10868 [2:04:10<01:00, 1.32s/it] 100%|█████████████████████████████████████████████████████████████████████████▋| 10823/10868 [2:04:10<00:43, 1.03it/s] 100%|█████████████████████████████████████████████████████████████████████████▋| 10824/10868 [2:04:10<00:31, 1.38it/s] 100%|█████████████████████████████████████████████████████████████████████████▋| 10827/10868 [2:04:10<00:22, 1.82it/s] 100%|█████████████████████████████████████████████████████████████████████████▋| 10830/10868 [2:04:11<00:15, 2.52it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10834/10868 [2:04:11<00:10, 3.37it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10836/10868 [2:04:12<00:12, 2.63it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10837/10868 [2:04:12<00:10, 2.97it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10838/10868 [2:04:12<00:09, 3.33it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10840/10868 [2:04:13<00:06, 4.06it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10841/10868 [2:04:13<00:05, 4.74it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10842/10868 [2:04:13<00:05, 4.74it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10844/10868 [2:04:13<00:04, 5.49it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10845/10868 [2:04:13<00:04, 5.48it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10846/10868 [2:04:14<00:04, 5.24it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10847/10868 [2:04:14<00:05, 3.89it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10848/10868 [2:04:15<00:06, 3.20it/s] 100%|█████████████████████████████████████████████████████████████████████████▊| 10849/10868 [2:04:15<00:05, 3.64it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10850/10868 [2:04:15<00:04, 4.03it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10851/10868 [2:04:15<00:05, 2.91it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10852/10868 [2:04:16<00:04, 3.58it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10854/10868 [2:04:16<00:03, 4.45it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10855/10868 [2:04:16<00:02, 4.69it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10856/10868 [2:04:16<00:02, 5.40it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10857/10868 [2:04:16<00:02, 5.45it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10858/10868 [2:04:16<00:01, 5.65it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10859/10868 [2:04:17<00:02, 3.92it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10860/10868 [2:04:17<00:01, 4.25it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10862/10868 [2:04:17<00:01, 4.70it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10863/10868 [2:04:18<00:01, 3.60it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10864/10868 [2:04:18<00:01, 3.06it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10865/10868 [2:04:18<00:00, 3.81it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10866/10868 [2:04:18<00:00, 4.66it/s] 100%|█████████████████████████████████████████████████████████████████████████▉| 10867/10868 [2:04:19<00:00, 5.25it/s] 100%|██████████████████████████████████████████████████████████████████████████| 10868/10868 [2:04:19<00:00, 5.37it/s]
5.3 Extracting top 1000 features from byte bigram files dataset¶
In [0]:
import dask.dataframe as dd
bytes_bigram_data = dd.read_csv(data_path+"result_bigram.csv",sample=512000000)
bytes_bigram_data.head()
Out[0]:
| ID | 00 00 | 00 01 | 00 02 | 00 03 | 00 04 | 00 05 | 00 06 | 00 07 | 00 08 | 00 09 | 00 0a | 00 0b | 00 0c | 00 0d | 00 0e | 00 0f | 00 10 | 00 11 | 00 12 | 00 13 | 00 14 | 00 15 | 00 16 | 00 17 | 00 18 | 00 19 | 00 1a | 00 1b | 00 1c | 00 1d | 00 1e | 00 1f | 00 20 | 00 21 | 00 22 | 00 23 | 00 24 | 00 25 | 00 26 | ... | ?? d9 | ?? da | ?? db | ?? dc | ?? dd | ?? de | ?? df | ?? e0 | ?? e1 | ?? e2 | ?? e3 | ?? e4 | ?? e5 | ?? e6 | ?? e7 | ?? e8 | ?? e9 | ?? ea | ?? eb | ?? ec | ?? ed | ?? ee | ?? ef | ?? f0 | ?? f1 | ?? f2 | ?? f3 | ?? f4 | ?? f5 | ?? f6 | ?? f7 | ?? f8 | ?? f9 | ?? fa | ?? fb | ?? fc | ?? fd | ?? fe | ?? ff | ?? ?? | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 274425 | 1269 | 1029 | 1469 | 1227 | 1144 | 1437 | 1263 | 1174 | 1270 | 1264 | 1437 | 1581 | 1282 | 1311 | 1176 | 1253 | 1087 | 1375 | 1050 | 1265 | 1358 | 1252 | 1396 | 1097 | 1123 | 1376 | 1261 | 1425 | 1435 | 1046 | 1247 | 1102 | 1254 | 1105 | 1657 | 1619 | 1408 | 1242 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1819 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 21075 | 752 | 73 | 48 | 175 | 12 | 10 | 11 | 42 | 9 | 16 | 11 | 31 | 18 | 7 | 61 | 422 | 9 | 3691 | 3 | 16 | 5 | 162 | 0 | 15 | 6 | 15 | 3 | 17 | 6 | 7 | 5 | 553 | 9 | 15 | 22 | 24 | 12 | 23 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8580 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 16798 | 596 | 159 | 144 | 513 | 595 | 557 | 146 | 528 | 154 | 155 | 151 | 529 | 169 | 159 | 200 | 173 | 201 | 143 | 175 | 150 | 199 | 180 | 152 | 182 | 137 | 156 | 185 | 155 | 156 | 140 | 173 | 160 | 164 | 218 | 136 | 137 | 146 | 158 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 459 |
| 3 | 01kcPWA9K2BOxQeS5Rju | 10417 | 225 | 61 | 69 | 114 | 40 | 25 | 22 | 63 | 15 | 21 | 19 | 70 | 14 | 14 | 62 | 215 | 23 | 11 | 11 | 47 | 21 | 9 | 11 | 21 | 7 | 8 | 7 | 31 | 9 | 13 | 19 | 177 | 5 | 11 | 3 | 42 | 61 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13932 |
| 4 | 01SuzwMJEIXsK7A8dQbl | 16271 | 62 | 22 | 126 | 9 | 11 | 3 | 5 | 11 | 3 | 1 | 3 | 9 | 1 | 4 | 15 | 6 | 3 | 2 | 4 | 4 | 0 | 2 | 2 | 5 | 23 | 5 | 2 | 0 | 2 | 0 | 5 | 7 | 1 | 6 | 2 | 5 | 6 | 3 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9000 |
5 rows × 66050 columns
In [0]:
from sklearn.feature_selection import SelectKBest, chi2
Y_labels=dd.read_csv(data_path+"trainLabels.csv")
Y_labels=Y_labels.rename(columns=dict(zip(Y_labels.columns, ["ID","Class"])))
bytes_bigram_data_withlabel = dd.merge(bytes_bigram_data, Y_labels,on='ID', how='left')
In [0]:
bytes_bigram_data_withlabel.head()
Out[0]:
| ID | 00 00 | 00 01 | 00 02 | 00 03 | 00 04 | 00 05 | 00 06 | 00 07 | 00 08 | 00 09 | 00 0a | 00 0b | 00 0c | 00 0d | 00 0e | 00 0f | 00 10 | 00 11 | 00 12 | 00 13 | 00 14 | 00 15 | 00 16 | 00 17 | 00 18 | 00 19 | 00 1a | 00 1b | 00 1c | 00 1d | 00 1e | 00 1f | 00 20 | 00 21 | 00 22 | 00 23 | 00 24 | 00 25 | 00 26 | ... | ?? da | ?? db | ?? dc | ?? dd | ?? de | ?? df | ?? e0 | ?? e1 | ?? e2 | ?? e3 | ?? e4 | ?? e5 | ?? e6 | ?? e7 | ?? e8 | ?? e9 | ?? ea | ?? eb | ?? ec | ?? ed | ?? ee | ?? ef | ?? f0 | ?? f1 | ?? f2 | ?? f3 | ?? f4 | ?? f5 | ?? f6 | ?? f7 | ?? f8 | ?? f9 | ?? fa | ?? fb | ?? fc | ?? fd | ?? fe | ?? ff | ?? ?? | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 274425 | 1269 | 1029 | 1469 | 1227 | 1144 | 1437 | 1263 | 1174 | 1270 | 1264 | 1437 | 1581 | 1282 | 1311 | 1176 | 1253 | 1087 | 1375 | 1050 | 1265 | 1358 | 1252 | 1396 | 1097 | 1123 | 1376 | 1261 | 1425 | 1435 | 1046 | 1247 | 1102 | 1254 | 1105 | 1657 | 1619 | 1408 | 1242 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1819 | 9 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 21075 | 752 | 73 | 48 | 175 | 12 | 10 | 11 | 42 | 9 | 16 | 11 | 31 | 18 | 7 | 61 | 422 | 9 | 3691 | 3 | 16 | 5 | 162 | 0 | 15 | 6 | 15 | 3 | 17 | 6 | 7 | 5 | 553 | 9 | 15 | 22 | 24 | 12 | 23 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8580 | 2 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 16798 | 596 | 159 | 144 | 513 | 595 | 557 | 146 | 528 | 154 | 155 | 151 | 529 | 169 | 159 | 200 | 173 | 201 | 143 | 175 | 150 | 199 | 180 | 152 | 182 | 137 | 156 | 185 | 155 | 156 | 140 | 173 | 160 | 164 | 218 | 136 | 137 | 146 | 158 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 459 | 9 |
| 3 | 01kcPWA9K2BOxQeS5Rju | 10417 | 225 | 61 | 69 | 114 | 40 | 25 | 22 | 63 | 15 | 21 | 19 | 70 | 14 | 14 | 62 | 215 | 23 | 11 | 11 | 47 | 21 | 9 | 11 | 21 | 7 | 8 | 7 | 31 | 9 | 13 | 19 | 177 | 5 | 11 | 3 | 42 | 61 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13932 | 1 |
| 4 | 01SuzwMJEIXsK7A8dQbl | 16271 | 62 | 22 | 126 | 9 | 11 | 3 | 5 | 11 | 3 | 1 | 3 | 9 | 1 | 4 | 15 | 6 | 3 | 2 | 4 | 4 | 0 | 2 | 2 | 5 | 23 | 5 | 2 | 0 | 2 | 0 | 5 | 7 | 1 | 6 | 2 | 5 | 6 | 3 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9000 | 8 |
5 rows × 66051 columns
In [0]:
top_k_features=SelectKBest(chi2, k=1000).fit(bytes_bigram_data_withlabel.drop(["ID","Class"], axis=1),bytes_bigram_data_withlabel["Class"])
#Creating dataframe for 1000 best features
df_scores=pd.DataFrame(top_k_features.scores_)
df_columns=pd.DataFrame(bytes_bigram_data.columns[1:])
feature_score_df=pd.concat([df_columns,df_scores],axis=1)
feature_score_df.columns=["Feature_Name","Feature_Score"]
#Let's look at the top 1000 features along with their scores + Save the feature score DF
feature_score_df=feature_score_df.nlargest(1000,"Feature_Score")
feature_score_df.to_csv(data_path+"byte_bigram_top_k.csv", index=None)
feature_score_df.head()
Out[0]:
| Feature_Name | Feature_Score | |
|---|---|---|
| 66048 | ?? ?? | 1.008849e+10 |
| 52632 | cc cc | 3.254310e+08 |
| 0 | 00 00 | 1.731621e+08 |
| 65790 | ff ff | 2.790252e+07 |
| 255 | 00 ff | 9.768750e+06 |
In [0]:
feature_score_df = pd.read_csv(data_path + "byte_bigram_top_k.csv")
In [0]:
#Getting top 1000 columns that have high value
cols_to_recover = list(feature_score_df["Feature_Name"])
cols_to_recover.insert(0,"ID")
#Getting the redused version of the dataframe
reduced_byte_bigram_df=bytes_bigram_data[cols_to_recover]
reduced_byte_bigram_df.head()
Out[0]:
| ID | ?? ?? | cc cc | 00 00 | ff ff | 00 ff | ff 00 | 02 02 | 10 10 | f0 f0 | 06 06 | 04 04 | f0 01 | 01 f0 | 03 03 | 90 f7 | 14 14 | 18 18 | 01 00 | 46 46 | 01 01 | 33 33 | 20 20 | 0b 0b | 4d 4d | 0f 0f | 53 53 | 5d 5d | 48 48 | fd 8b | 44 24 | 56 56 | fc fc | 75 75 | 3b 3b | 59 59 | 5e 5e | c7 c7 | 45 45 | 55 55 | ... | f8 04 | eb 04 | 30 03 | 04 c0 | 53 06 | 04 64 | 64 04 | dc dc | d2 00 | 04 80 | 07 00 | 45 fc | 0b 03 | 4d 06 | 4c 00 | 48 06 | a0 00 | 43 12 | 98 04 | 0f 12 | 8b ff | 06 4d | 04 98 | 86 00 | 00 86 | e4 00 | 22 00 | 18 03 | 03 0f | 38 00 | 8a 00 | 29 00 | 06 c3 | ba 00 | 06 44 | 8b c1 | b8 00 | 00 38 | 06 ec | 6f 02 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 1819 | 99 | 274425 | 876 | 1906 | 1502 | 74 | 98 | 84 | 93 | 97 | 11 | 4 | 103 | 5 | 79 | 102 | 1791 | 98 | 76 | 73 | 78 | 69 | 88 | 90 | 78 | 99 | 104 | 7 | 404 | 78 | 86 | 71 | 89 | 100 | 80 | 97 | 85 | 93 | ... | 10 | 15 | 9 | 9 | 7 | 8 | 6 | 93 | 1085 | 16 | 1282 | 5 | 9 | 3 | 1259 | 8 | 1246 | 11 | 4 | 8 | 8 | 9 | 8 | 1116 | 1127 | 1085 | 1111 | 9 | 11 | 1232 | 1289 | 1427 | 6 | 1054 | 4 | 27 | 1241 | 1317 | 3 | 7 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 8580 | 732 | 21075 | 4861 | 1036 | 2202 | 23 | 195 | 21 | 8 | 2 | 1 | 0 | 11 | 3 | 2 | 0 | 829 | 3 | 157 | 1 | 57 | 7 | 4 | 0 | 29 | 0 | 14 | 5 | 78 | 20 | 18 | 0 | 2 | 52 | 0 | 7 | 1 | 0 | ... | 2 | 6 | 0 | 0 | 0 | 6 | 1 | 56 | 2 | 6 | 44 | 92 | 0 | 0 | 18 | 1 | 6 | 4602 | 1 | 2777 | 250 | 0 | 0 | 12 | 10 | 15 | 16 | 1 | 3 | 19 | 2 | 9 | 0 | 7 | 0 | 55 | 15 | 24 | 0 | 0 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 459 | 7 | 16798 | 2358 | 3076 | 2960 | 4 | 5 | 3 | 81 | 68 | 19 | 21 | 1 | 5 | 3 | 7 | 597 | 6 | 71 | 3 | 1 | 9 | 3 | 6 | 5 | 5 | 3 | 39 | 2 | 64 | 4 | 59 | 6 | 77 | 6 | 7 | 8 | 5 | ... | 17 | 15 | 5 | 66 | 17 | 18 | 12 | 5 | 153 | 17 | 171 | 5 | 5 | 16 | 149 | 13 | 151 | 7 | 18 | 10 | 776 | 18 | 11 | 147 | 156 | 180 | 216 | 4 | 6 | 168 | 158 | 168 | 12 | 146 | 22 | 52 | 132 | 128 | 26 | 8 |
| 3 | 01kcPWA9K2BOxQeS5Rju | 13932 | 22 | 10417 | 1363 | 694 | 698 | 3 | 10 | 11 | 6 | 26 | 9 | 5 | 10 | 5 | 4 | 1 | 228 | 8 | 33 | 75 | 12 | 0 | 4 | 4 | 4 | 0 | 5 | 10 | 33 | 4 | 16 | 9 | 54 | 45 | 3 | 1 | 94 | 11 | ... | 4 | 6 | 1 | 7 | 1 | 6 | 2 | 1 | 9 | 7 | 26 | 67 | 1 | 3 | 30 | 2 | 20 | 2 | 5 | 3 | 384 | 2 | 1 | 4 | 3 | 33 | 23 | 3 | 8 | 17 | 13 | 12 | 3 | 52 | 3 | 10 | 55 | 25 | 1 | 2 |
| 4 | 01SuzwMJEIXsK7A8dQbl | 9000 | 31 | 16271 | 76 | 64 | 24 | 0 | 2 | 1 | 1 | 0 | 6 | 0 | 1 | 1 | 1 | 0 | 231 | 1 | 1 | 3 | 1 | 2 | 2 | 1 | 3 | 2 | 0 | 0 | 0 | 0 | 3 | 3 | 1 | 4 | 2 | 3 | 1 | 0 | ... | 0 | 1 | 2 | 1 | 2 | 0 | 0 | 0 | 3 | 3 | 17 | 166 | 0 | 1 | 11 | 2 | 4 | 0 | 1 | 0 | 41 | 0 | 2 | 7 | 3 | 5 | 6 | 1 | 2 | 6 | 15 | 4 | 0 | 1 | 0 | 2 | 5 | 9 | 0 | 0 |
5 rows × 1001 columns
In [0]:
df = reduced_byte_bigram_df.compute()
df.head()
Out[0]:
| ID | ?? ?? | cc cc | 00 00 | ff ff | 00 ff | ff 00 | 02 02 | 10 10 | f0 f0 | 06 06 | 04 04 | f0 01 | 01 f0 | 03 03 | 90 f7 | 14 14 | 18 18 | 01 00 | 46 46 | 01 01 | 33 33 | 20 20 | 0b 0b | 4d 4d | 0f 0f | 53 53 | 5d 5d | 48 48 | fd 8b | 44 24 | 56 56 | fc fc | 75 75 | 3b 3b | 59 59 | 5e 5e | c7 c7 | 45 45 | 55 55 | ... | f8 04 | eb 04 | 30 03 | 04 c0 | 53 06 | 04 64 | 64 04 | dc dc | d2 00 | 04 80 | 07 00 | 45 fc | 0b 03 | 4d 06 | 4c 00 | 48 06 | a0 00 | 43 12 | 98 04 | 0f 12 | 8b ff | 06 4d | 04 98 | 86 00 | 00 86 | e4 00 | 22 00 | 18 03 | 03 0f | 38 00 | 8a 00 | 29 00 | 06 c3 | ba 00 | 06 44 | 8b c1 | b8 00 | 00 38 | 06 ec | 6f 02 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 1819 | 99 | 274425 | 876 | 1906 | 1502 | 74 | 98 | 84 | 93 | 97 | 11 | 4 | 103 | 5 | 79 | 102 | 1791 | 98 | 76 | 73 | 78 | 69 | 88 | 90 | 78 | 99 | 104 | 7 | 404 | 78 | 86 | 71 | 89 | 100 | 80 | 97 | 85 | 93 | ... | 10 | 15 | 9 | 9 | 7 | 8 | 6 | 93 | 1085 | 16 | 1282 | 5 | 9 | 3 | 1259 | 8 | 1246 | 11 | 4 | 8 | 8 | 9 | 8 | 1116 | 1127 | 1085 | 1111 | 9 | 11 | 1232 | 1289 | 1427 | 6 | 1054 | 4 | 27 | 1241 | 1317 | 3 | 7 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 8580 | 732 | 21075 | 4861 | 1036 | 2202 | 23 | 195 | 21 | 8 | 2 | 1 | 0 | 11 | 3 | 2 | 0 | 829 | 3 | 157 | 1 | 57 | 7 | 4 | 0 | 29 | 0 | 14 | 5 | 78 | 20 | 18 | 0 | 2 | 52 | 0 | 7 | 1 | 0 | ... | 2 | 6 | 0 | 0 | 0 | 6 | 1 | 56 | 2 | 6 | 44 | 92 | 0 | 0 | 18 | 1 | 6 | 4602 | 1 | 2777 | 250 | 0 | 0 | 12 | 10 | 15 | 16 | 1 | 3 | 19 | 2 | 9 | 0 | 7 | 0 | 55 | 15 | 24 | 0 | 0 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 459 | 7 | 16798 | 2358 | 3076 | 2960 | 4 | 5 | 3 | 81 | 68 | 19 | 21 | 1 | 5 | 3 | 7 | 597 | 6 | 71 | 3 | 1 | 9 | 3 | 6 | 5 | 5 | 3 | 39 | 2 | 64 | 4 | 59 | 6 | 77 | 6 | 7 | 8 | 5 | ... | 17 | 15 | 5 | 66 | 17 | 18 | 12 | 5 | 153 | 17 | 171 | 5 | 5 | 16 | 149 | 13 | 151 | 7 | 18 | 10 | 776 | 18 | 11 | 147 | 156 | 180 | 216 | 4 | 6 | 168 | 158 | 168 | 12 | 146 | 22 | 52 | 132 | 128 | 26 | 8 |
| 3 | 01kcPWA9K2BOxQeS5Rju | 13932 | 22 | 10417 | 1363 | 694 | 698 | 3 | 10 | 11 | 6 | 26 | 9 | 5 | 10 | 5 | 4 | 1 | 228 | 8 | 33 | 75 | 12 | 0 | 4 | 4 | 4 | 0 | 5 | 10 | 33 | 4 | 16 | 9 | 54 | 45 | 3 | 1 | 94 | 11 | ... | 4 | 6 | 1 | 7 | 1 | 6 | 2 | 1 | 9 | 7 | 26 | 67 | 1 | 3 | 30 | 2 | 20 | 2 | 5 | 3 | 384 | 2 | 1 | 4 | 3 | 33 | 23 | 3 | 8 | 17 | 13 | 12 | 3 | 52 | 3 | 10 | 55 | 25 | 1 | 2 |
| 4 | 01SuzwMJEIXsK7A8dQbl | 9000 | 31 | 16271 | 76 | 64 | 24 | 0 | 2 | 1 | 1 | 0 | 6 | 0 | 1 | 1 | 1 | 0 | 231 | 1 | 1 | 3 | 1 | 2 | 2 | 1 | 3 | 2 | 0 | 0 | 0 | 0 | 3 | 3 | 1 | 4 | 2 | 3 | 1 | 0 | ... | 0 | 1 | 2 | 1 | 2 | 0 | 0 | 0 | 3 | 3 | 17 | 166 | 0 | 1 | 11 | 2 | 4 | 0 | 1 | 0 | 41 | 0 | 2 | 7 | 3 | 5 | 6 | 1 | 2 | 6 | 15 | 4 | 0 | 1 | 0 | 2 | 5 | 9 | 0 | 0 |
5 rows × 1001 columns
In [0]:
#Saving the dataframe
df.to_csv(data_path+"reduced_byte_bigram_df.csv",index=None)
print("Saved the Reduced dataframe")
Saved the Reduced dataframe
5.4 Getting top 800 pixel values from image features¶
In [0]:
import cv2
from tqdm import tqdm
image_features_matrix = np.zeros((10868, 800))
all_ids = []
for i, image in tqdm(enumerate(os.listdir("image_feature"))):
all_ids.append(image.split('.')[0])
img = cv2.imread("image_feature/" + asmfile)
image_features_matrix[i, :] += img.flatten()[:800]
10868it [06:12, 29.21it/s]
In [0]:
from sklearn.preprocessing import normalize
name_image_features = []
for i in range(800):
name_image_features.append('pixel' + str(i))
img_df = pd.DataFrame(normalize(image_features_matrix, axis = 0), columns = name_image_features)
img_df['ID'] = all_ids
img_df.head()
Out[0]:
| pixel0 | pixel1 | pixel2 | pixel3 | pixel4 | pixel5 | pixel6 | pixel7 | pixel8 | pixel9 | ... | pixel791 | pixel792 | pixel793 | pixel794 | pixel795 | pixel796 | pixel797 | pixel798 | pixel799 | ID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 01azqd4InC7m9JpocGv5 |
| 1 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 01IsoiSMh5gxyDYTl4CB |
| 2 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 01jsnpXSAlgw6aPeDxrU |
| 3 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 01kcPWA9K2BOxQeS5Rju |
| 4 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 01SuzwMJEIXsK7A8dQbl |
5 rows × 801 columns
In [0]:
img_df.to_csv('image_features.csv', index=False)
5.5 Merging Bytes file data(unigram+bigram), asm unigram data, image data to a single data frame¶
In [0]:
bytes_unigram_data = pd.read_csv(data_path + "result_with_size.csv").drop('Unnamed: 0', axis=1)
bytes_unigram_data.head(3)
Out[0]:
| ID | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 0a | 0b | 0c | 0d | 0e | 0f | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 1a | 1b | 1c | 1d | 1e | 1f | 20 | 21 | 22 | 23 | 24 | 25 | 26 | ... | db | dc | dd | de | df | e0 | e1 | e2 | e3 | e4 | e5 | e6 | e7 | e8 | e9 | ea | eb | ec | ed | ee | ef | f0 | f1 | f2 | f3 | f4 | f5 | f6 | f7 | f8 | f9 | fa | fb | fc | fd | fe | ff | ?? | size | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 601905 | 3905 | 2816 | 3832 | 3345 | 3242 | 3650 | 3201 | 2965 | 3205 | 3211 | 3546 | 4038 | 4096 | 3218 | 3032 | 3269 | 2740 | 3459 | 2767 | 3354 | 4049 | 3184 | 3495 | 3139 | 2810 | 3444 | 3130 | 3786 | 3682 | 2665 | 3100 | 2990 | 3175 | 2826 | 4295 | 8565 | 4002 | 3105 | ... | 3438 | 2773 | 2778 | 3467 | 2753 | 3097 | 3504 | 2764 | 2746 | 2913 | 3179 | 3088 | 2678 | 4710 | 3034 | 3127 | 3409 | 3571 | 2669 | 3523 | 3476 | 3685 | 3138 | 2727 | 2808 | 3209 | 3233 | 3271 | 2804 | 3687 | 3101 | 3211 | 3097 | 2758 | 3099 | 2759 | 5753 | 1824 | 4.234863 | 9 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 39755 | 8337 | 7249 | 7186 | 8663 | 6844 | 8420 | 7589 | 9291 | 358 | 340 | 6649 | 8660 | 447 | 218 | 6869 | 8869 | 228 | 352581 | 187 | 12034 | 353 | 349 | 154 | 6735 | 307 | 278 | 209 | 688 | 266 | 321 | 213 | 12372 | 309 | 196 | 296 | 8979 | 301 | 288 | ... | 547 | 617 | 452 | 504 | 449 | 503 | 400 | 401 | 797 | 497 | 353 | 368 | 312 | 7506 | 6878 | 394 | 6910 | 8206 | 223 | 385 | 442 | 6813 | 484 | 424 | 352 | 519 | 265 | 290 | 451 | 6536 | 439 | 281 | 302 | 7639 | 518 | 17001 | 54902 | 8588 | 5.538818 | 2 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 93506 | 9542 | 2568 | 2438 | 8925 | 9330 | 9007 | 2342 | 9107 | 2457 | 2655 | 2669 | 9113 | 2584 | 2788 | 2487 | 2782 | 2611 | 2301 | 2706 | 2968 | 2808 | 2408 | 2416 | 2677 | 2327 | 2788 | 2972 | 2659 | 2433 | 2267 | 2893 | 2575 | 2482 | 2801 | 2565 | 2524 | 2327 | 2546 | ... | 2572 | 2565 | 2675 | 2473 | 2642 | 2456 | 2728 | 2369 | 2552 | 2746 | 2511 | 2246 | 2454 | 22304 | 2514 | 2804 | 2660 | 3042 | 2554 | 2520 | 2755 | 2619 | 2491 | 2537 | 2409 | 2456 | 2685 | 2598 | 2325 | 2358 | 2242 | 2885 | 2863 | 2471 | 2786 | 2680 | 49144 | 468 | 3.887939 | 9 |
3 rows × 260 columns
In [0]:
bytes_bigram_data = pd.read_csv(data_path + "reduced_byte_bigram_df.csv")
bytes_bigram_data.head(3)
Out[0]:
| ID | ?? ?? | cc cc | 00 00 | ff ff | 00 ff | ff 00 | 02 02 | 10 10 | f0 f0 | 06 06 | 04 04 | f0 01 | 01 f0 | 03 03 | 90 f7 | 14 14 | 18 18 | 01 00 | 46 46 | 01 01 | 33 33 | 20 20 | 0b 0b | 4d 4d | 0f 0f | 53 53 | 5d 5d | 48 48 | fd 8b | 44 24 | 56 56 | fc fc | 75 75 | 3b 3b | 59 59 | 5e 5e | c7 c7 | 45 45 | 55 55 | ... | f8 04 | eb 04 | 30 03 | 04 c0 | 53 06 | 04 64 | 64 04 | dc dc | d2 00 | 04 80 | 07 00 | 45 fc | 0b 03 | 4d 06 | 4c 00 | 48 06 | a0 00 | 43 12 | 98 04 | 0f 12 | 8b ff | 06 4d | 04 98 | 86 00 | 00 86 | e4 00 | 22 00 | 18 03 | 03 0f | 38 00 | 8a 00 | 29 00 | 06 c3 | ba 00 | 06 44 | 8b c1 | b8 00 | 00 38 | 06 ec | 6f 02 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 1819 | 99 | 274425 | 876 | 1906 | 1502 | 74 | 98 | 84 | 93 | 97 | 11 | 4 | 103 | 5 | 79 | 102 | 1791 | 98 | 76 | 73 | 78 | 69 | 88 | 90 | 78 | 99 | 104 | 7 | 404 | 78 | 86 | 71 | 89 | 100 | 80 | 97 | 85 | 93 | ... | 10 | 15 | 9 | 9 | 7 | 8 | 6 | 93 | 1085 | 16 | 1282 | 5 | 9 | 3 | 1259 | 8 | 1246 | 11 | 4 | 8 | 8 | 9 | 8 | 1116 | 1127 | 1085 | 1111 | 9 | 11 | 1232 | 1289 | 1427 | 6 | 1054 | 4 | 27 | 1241 | 1317 | 3 | 7 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 8580 | 732 | 21075 | 4861 | 1036 | 2202 | 23 | 195 | 21 | 8 | 2 | 1 | 0 | 11 | 3 | 2 | 0 | 829 | 3 | 157 | 1 | 57 | 7 | 4 | 0 | 29 | 0 | 14 | 5 | 78 | 20 | 18 | 0 | 2 | 52 | 0 | 7 | 1 | 0 | ... | 2 | 6 | 0 | 0 | 0 | 6 | 1 | 56 | 2 | 6 | 44 | 92 | 0 | 0 | 18 | 1 | 6 | 4602 | 1 | 2777 | 250 | 0 | 0 | 12 | 10 | 15 | 16 | 1 | 3 | 19 | 2 | 9 | 0 | 7 | 0 | 55 | 15 | 24 | 0 | 0 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 459 | 7 | 16798 | 2358 | 3076 | 2960 | 4 | 5 | 3 | 81 | 68 | 19 | 21 | 1 | 5 | 3 | 7 | 597 | 6 | 71 | 3 | 1 | 9 | 3 | 6 | 5 | 5 | 3 | 39 | 2 | 64 | 4 | 59 | 6 | 77 | 6 | 7 | 8 | 5 | ... | 17 | 15 | 5 | 66 | 17 | 18 | 12 | 5 | 153 | 17 | 171 | 5 | 5 | 16 | 149 | 13 | 151 | 7 | 18 | 10 | 776 | 18 | 11 | 147 | 156 | 180 | 216 | 4 | 6 | 168 | 158 | 168 | 12 | 146 | 22 | 52 | 132 | 128 | 26 | 8 |
3 rows × 1001 columns
In [0]:
asm_unigram_data = pd.read_csv(data_path + "asmoutputfile.csv")
asm_unigram_data.head(3)
Out[0]:
| ID | HEADER: | .text: | .Pav: | .idata: | .data: | .bss: | .rdata: | .edata: | .rsrc: | .tls: | .reloc: | .BSS: | .CODE | jmp | mov | retf | push | pop | xor | retn | nop | sub | inc | dec | add | imul | xchg | or | shr | cmp | call | shl | ror | rol | jnb | jz | rtn | lea | movzx | .dll | std:: | :dword | edx | esi | eax | ebx | ecx | edi | ebp | esp | eip | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01kcPWA9K2BOxQeS5Rju | 19 | 744 | 0 | 127 | 57 | 0 | 323 | 0 | 3 | 0 | 3 | 0 | 0 | 12 | 89 | 0 | 81 | 19 | 18 | 16 | 0 | 5 | 6 | 3 | 11 | 0 | 0 | 6 | 0 | 15 | 53 | 1 | 0 | 0 | 5 | 13 | 0 | 36 | 1 | 23 | 35 | 137 | 18 | 66 | 15 | 43 | 83 | 0 | 17 | 48 | 29 |
| 1 | 1E93CpP60RHFNiT5Qfvn | 17 | 838 | 0 | 103 | 49 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 4 | 88 | 0 | 79 | 26 | 19 | 9 | 0 | 8 | 4 | 4 | 6 | 1 | 0 | 0 | 0 | 19 | 29 | 0 | 0 | 0 | 1 | 7 | 0 | 20 | 1 | 12 | 64 | 130 | 18 | 29 | 48 | 82 | 12 | 0 | 14 | 0 | 20 |
| 2 | 3ekVow2ajZHbTnBcsDfX | 17 | 427 | 0 | 50 | 43 | 0 | 145 | 0 | 3 | 0 | 3 | 0 | 0 | 5 | 73 | 0 | 53 | 18 | 9 | 7 | 0 | 5 | 2 | 1 | 2 | 1 | 0 | 4 | 0 | 14 | 21 | 2 | 0 | 0 | 0 | 10 | 0 | 4 | 1 | 36 | 13 | 84 | 13 | 42 | 10 | 67 | 14 | 0 | 11 | 0 | 9 |
In [0]:
asm_image_data = pd.read_csv(data_path + "image_features.csv")
asm_image_data.head(3)
Out[0]:
| pixel0 | pixel1 | pixel2 | pixel3 | pixel4 | pixel5 | pixel6 | pixel7 | pixel8 | pixel9 | pixel10 | pixel11 | pixel12 | pixel13 | pixel14 | pixel15 | pixel16 | pixel17 | pixel18 | pixel19 | pixel20 | pixel21 | pixel22 | pixel23 | pixel24 | pixel25 | pixel26 | pixel27 | pixel28 | pixel29 | pixel30 | pixel31 | pixel32 | pixel33 | pixel34 | pixel35 | pixel36 | pixel37 | pixel38 | pixel39 | ... | pixel761 | pixel762 | pixel763 | pixel764 | pixel765 | pixel766 | pixel767 | pixel768 | pixel769 | pixel770 | pixel771 | pixel772 | pixel773 | pixel774 | pixel775 | pixel776 | pixel777 | pixel778 | pixel779 | pixel780 | pixel781 | pixel782 | pixel783 | pixel784 | pixel785 | pixel786 | pixel787 | pixel788 | pixel789 | pixel790 | pixel791 | pixel792 | pixel793 | pixel794 | pixel795 | pixel796 | pixel797 | pixel798 | pixel799 | ID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 01azqd4InC7m9JpocGv5 |
| 1 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 01IsoiSMh5gxyDYTl4CB |
| 2 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 01jsnpXSAlgw6aPeDxrU |
3 rows × 801 columns
In [0]:
combined_data = bytes_unigram_data
all_dataframes = [bytes_bigram_data, asm_unigram_data, asm_image_data]
for data in all_dataframes:
combined_data=pd.merge(combined_data,data,on="ID",how="left")
print(combined_data.shape)
combined_data.head(3)
(10868, 2111)
Out[0]:
| ID | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 0a | 0b | 0c | 0d | 0e | 0f | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 1a | 1b | 1c | 1d | 1e | 1f | 20 | 21 | 22 | 23 | 24 | 25 | 26 | ... | pixel760 | pixel761 | pixel762 | pixel763 | pixel764 | pixel765 | pixel766 | pixel767 | pixel768 | pixel769 | pixel770 | pixel771 | pixel772 | pixel773 | pixel774 | pixel775 | pixel776 | pixel777 | pixel778 | pixel779 | pixel780 | pixel781 | pixel782 | pixel783 | pixel784 | pixel785 | pixel786 | pixel787 | pixel788 | pixel789 | pixel790 | pixel791 | pixel792 | pixel793 | pixel794 | pixel795 | pixel796 | pixel797 | pixel798 | pixel799 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 601905 | 3905 | 2816 | 3832 | 3345 | 3242 | 3650 | 3201 | 2965 | 3205 | 3211 | 3546 | 4038 | 4096 | 3218 | 3032 | 3269 | 2740 | 3459 | 2767 | 3354 | 4049 | 3184 | 3495 | 3139 | 2810 | 3444 | 3130 | 3786 | 3682 | 2665 | 3100 | 2990 | 3175 | 2826 | 4295 | 8565 | 4002 | 3105 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 39755 | 8337 | 7249 | 7186 | 8663 | 6844 | 8420 | 7589 | 9291 | 358 | 340 | 6649 | 8660 | 447 | 218 | 6869 | 8869 | 228 | 352581 | 187 | 12034 | 353 | 349 | 154 | 6735 | 307 | 278 | 209 | 688 | 266 | 321 | 213 | 12372 | 309 | 196 | 296 | 8979 | 301 | 288 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 93506 | 9542 | 2568 | 2438 | 8925 | 9330 | 9007 | 2342 | 9107 | 2457 | 2655 | 2669 | 9113 | 2584 | 2788 | 2487 | 2782 | 2611 | 2301 | 2706 | 2968 | 2808 | 2408 | 2416 | 2677 | 2327 | 2788 | 2972 | 2659 | 2433 | 2267 | 2893 | 2575 | 2482 | 2801 | 2565 | 2524 | 2327 | 2546 | ... | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 | 0.009592 |
3 rows × 2111 columns
In [0]:
data_for_normalization = combined_data.drop(['ID', 'Class'], axis=1)
from sklearn.preprocessing import MinMaxScaler
data_for_normalization = MinMaxScaler().fit_transform(data_for_normalization)
data_for_normalization.shape
Out[0]:
(10868, 2109)
In [0]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(data_for_normalization, combined_data['Class'], stratify=combined_data['Class'], test_size=0.20)
X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train, stratify=y_train, test_size=0.20)
5.6 Applying XGBoost¶
5.6.1 XGBoost Without Hyperparameter Tuning¶
5.6.2 XGBoost With Hyperparameter Tuning¶
In [0]:
x_cfl=XGBClassifier()
prams={
'learning_rate':[0.01,0.03,0.05,0.1,0.15,0.2],
'n_estimators':[100,200,500,1000,2000],
'max_depth':[3,5,10],
'colsample_bytree':[0.1,0.3,0.5,1],
'subsample':[0.1,0.3,0.5,1]
}
random_cfl=RandomizedSearchCV(x_cfl,param_distributions=prams,verbose=10,n_jobs=-1,)
random_cfl.fit(X_train,y_train)
Fitting 3 folds for each of 10 candidates, totalling 30 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers. [Parallel(n_jobs=-1)]: Done 5 tasks | elapsed: 26.1min [Parallel(n_jobs=-1)]: Done 10 tasks | elapsed: 30.2min [Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 59.9min [Parallel(n_jobs=-1)]: Done 27 out of 30 | elapsed: 77.5min remaining: 8.6min [Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 81.3min finished
Out[0]:
RandomizedSearchCV(cv='warn', error_score='raise-deprecating',
estimator=XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1,
colsample_bynode=1,
colsample_bytree=1, gamma=0,
learning_rate=0.1, max_delta_step=0,
max_depth=3, min_child_weight=1,
missing=None, n_estimators=100,
n_jobs=1, nthread=None,
objective='binary:logistic',
random_state=0, reg_al...
seed=None, silent=None, subsample=1,
verbosity=1),
iid='warn', n_iter=10, n_jobs=-1,
param_distributions={'colsample_bytree': [0.1, 0.3, 0.5, 1],
'learning_rate': [0.01, 0.03, 0.05, 0.1,
0.15, 0.2],
'max_depth': [3, 5, 10],
'n_estimators': [100, 200, 500, 1000,
2000],
'subsample': [0.1, 0.3, 0.5, 1]},
pre_dispatch='2*n_jobs', random_state=None, refit=True,
return_train_score=False, scoring=None, verbose=10)
In [0]:
random_cfl.best_params_
Out[0]:
{'colsample_bytree': 0.5,
'learning_rate': 0.05,
'max_depth': 3,
'n_estimators': 500,
'subsample': 1}
In [0]:
x_cfl=XGBClassifier(n_estimators=500,subsample=1,learning_rate=0.05,colsample_bytree=0.5,max_depth=3)
x_cfl.fit(X_train,y_train)
c_cfl=CalibratedClassifierCV(x_cfl,method='sigmoid')
c_cfl.fit(X_train,y_train)
predict_y = c_cfl.predict_proba(X_train)
print ('train loss',log_loss(y_train, predict_y))
predict_y = c_cfl.predict_proba(X_cv)
print ('cv loss',log_loss(y_cv, predict_y))
predict_y = c_cfl.predict_proba(X_test)
print ('test loss',log_loss(y_test , predict_y))
train loss 0.010640510100658435 cv loss 0.041460774460253425 test loss 0.02591651000860047
5.7 Extracting asm bi tri gram features for opcodes¶
In [0]:
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
len(opcodes)
Out[0]:
26
In [0]:
asm_bigram_vocab = []
for i in opcodes:
for j in opcodes:
asm_bigram_vocab.append(i + ' ' + j)
print(len(asm_bigram_vocab))
asm_trigram_vocab = []
for i in opcodes:
for j in opcodes:
for k in opcodes:
asm_trigram_vocab.append(i + ' ' + j+' '+k)
print(len(asm_trigram_vocab))
asm_opcode_vocab = asm_bigram_vocab+asm_trigram_vocab
print(len(asm_opcode_vocab))
676 17576 18252
In [0]:
#creating a folder which contains text files extracted from asm files having only opcodes
from tqdm import tqdm
files = os.listdir('asmFiles')
filenames=[]
array=[]
for file in tqdm(files):
if(file.endswith("asm")):
file=file.split('.')[0]
text_file = open('asmOpcodeOnlyFiles/'+file+".txt", 'w+')
with codecs.open('asmFiles/'+file+".asm", encoding='cp1252', errors ='replace') as fp:
for line in fp:
a=line.rstrip().split(" ")
b=''
for code in a:
if code in opcodes:
b=b + code + ' '
text_file.write(b)
fp.close()
text_file.close()
100%|██████████████████████████████████████████████████████████████████████████| 10868/10868 [8:59:20<00:00, 1.33s/it]
In [0]:
#creating csv file for asm bi-tri grams
files = os.listdir('asmOpcodeOnlyFiles')
from tqdm import tqdm
from nltk.util import ngrams
#program to convert into bag of words of asmfiles
#this is custom-built bag of words
asm_feature_file=open('asm_result_bi_trigram.csv','w+')
asm_feature_file.write("ID,"+"asmSize,"+','.join(asm_opcode_vocab)) #adding asm size as a new feature to capture size of text(opcode) asm files
asm_feature_file.write("\n")
for file in tqdm(files):
asm_feature_file.write(file.split('.')[0]+",")
asm_feature_file.write(str(os.stat('asmOpcodeOnlyFiles/'+file).st_size/1024) + ",") #Getting the size in kb's
if(file.endswith("txt")):
with open('asmOpcodeOnlyFiles/'+file,"r") as asm_file:
whole_file = asm_file.readline().rstrip().split(' ')
vocab_keys = dict(zip(asm_opcode_vocab[:], [0]*len(asm_opcode_vocab[:])))
bi_gram = [' '.join(i) for i in list(ngrams(whole_file, 2))] #converting whole file in bigrams('a b c' =>'a b','b c')
tri_gram = [' '.join(i) for i in list(ngrams(whole_file, 3))]
for hex_code in bi_gram:
vocab_keys[hex_code.lower()] += 1
for hex_code in tri_gram:
vocab_keys[hex_code.lower()] += 1
new_rows = [str(vocab_keys[i]) for i in asm_opcode_vocab[:]]
asm_feature_file.write(','.join(new_rows)) #addition of new row to the csv file
asm_feature_file.write("\n")
asm_feature_file.close()
100%|████████████████████████████████████████████████████████████████████████████| 10868/10868 [06:26<00:00, 28.11it/s]
5.8 Getting 1000 best bi-tri gram features from asm feature file¶
In [0]:
asm_feature_data = pd.read_csv(data_path + "asm_result_bi_trigram.csv",index_col=None)
asm_feature_data.head(3)
Out[0]:
| ID | asmSize | jmp jmp | jmp mov | jmp retf | jmp push | jmp pop | jmp xor | jmp retn | jmp nop | jmp sub | jmp inc | jmp dec | jmp add | jmp imul | jmp xchg | jmp or | jmp shr | jmp cmp | jmp call | jmp shl | jmp ror | jmp rol | jmp jnb | jmp jz | jmp rtn | jmp lea | jmp movzx | mov jmp | mov mov | mov retf | mov push | mov pop | mov xor | mov retn | mov nop | mov sub | mov inc | mov dec | mov add | ... | movzx lea imul | movzx lea xchg | movzx lea or | movzx lea shr | movzx lea cmp | movzx lea call | movzx lea shl | movzx lea ror | movzx lea rol | movzx lea jnb | movzx lea jz | movzx lea rtn | movzx lea lea | movzx lea movzx | movzx movzx jmp | movzx movzx mov | movzx movzx retf | movzx movzx push | movzx movzx pop | movzx movzx xor | movzx movzx retn | movzx movzx nop | movzx movzx sub | movzx movzx inc | movzx movzx dec | movzx movzx add | movzx movzx imul | movzx movzx xchg | movzx movzx or | movzx movzx shr | movzx movzx cmp | movzx movzx call | movzx movzx shl | movzx movzx ror | movzx movzx rol | movzx movzx jnb | movzx movzx jz | movzx movzx rtn | movzx movzx lea | movzx movzx movzx | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 63.905273 | 440 | 192 | 0 | 6 | 0 | 17 | 0 | 0 | 24 | 0 | 1 | 34 | 1 | 0 | 3 | 0 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 165 | 1747 | 0 | 843 | 202 | 196 | 101 | 0 | 253 | 2 | 0 | 219 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 20.499023 | 0 | 32 | 0 | 3 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 34 | 1157 | 0 | 240 | 66 | 31 | 37 | 3 | 104 | 8 | 4 | 114 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 7.018555 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | ... | 0 | 0 | 3 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
3 rows × 18254 columns
In [0]:
from sklearn.feature_selection import SelectKBest, chi2
Y_labels=pd.read_csv(data_path+"trainLabels.csv")
Y_labels.rename(columns = {'Id':'ID'}, inplace = True)
asm_feature_data_withlabel = pd.merge(asm_feature_data, Y_labels,on='ID', how='left')
asm_feature_data_withlabel.head(3)
Out[0]:
| ID | asmSize | jmp jmp | jmp mov | jmp retf | jmp push | jmp pop | jmp xor | jmp retn | jmp nop | jmp sub | jmp inc | jmp dec | jmp add | jmp imul | jmp xchg | jmp or | jmp shr | jmp cmp | jmp call | jmp shl | jmp ror | jmp rol | jmp jnb | jmp jz | jmp rtn | jmp lea | jmp movzx | mov jmp | mov mov | mov retf | mov push | mov pop | mov xor | mov retn | mov nop | mov sub | mov inc | mov dec | mov add | ... | movzx lea xchg | movzx lea or | movzx lea shr | movzx lea cmp | movzx lea call | movzx lea shl | movzx lea ror | movzx lea rol | movzx lea jnb | movzx lea jz | movzx lea rtn | movzx lea lea | movzx lea movzx | movzx movzx jmp | movzx movzx mov | movzx movzx retf | movzx movzx push | movzx movzx pop | movzx movzx xor | movzx movzx retn | movzx movzx nop | movzx movzx sub | movzx movzx inc | movzx movzx dec | movzx movzx add | movzx movzx imul | movzx movzx xchg | movzx movzx or | movzx movzx shr | movzx movzx cmp | movzx movzx call | movzx movzx shl | movzx movzx ror | movzx movzx rol | movzx movzx jnb | movzx movzx jz | movzx movzx rtn | movzx movzx lea | movzx movzx movzx | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 63.905273 | 440 | 192 | 0 | 6 | 0 | 17 | 0 | 0 | 24 | 0 | 1 | 34 | 1 | 0 | 3 | 0 | 1 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 165 | 1747 | 0 | 843 | 202 | 196 | 101 | 0 | 253 | 2 | 0 | 219 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 20.499023 | 0 | 32 | 0 | 3 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 34 | 1157 | 0 | 240 | 66 | 31 | 37 | 3 | 104 | 8 | 4 | 114 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 7.018555 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | ... | 0 | 3 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 9 |
3 rows × 18255 columns
In [11]:
top_k_features=SelectKBest(chi2, k=1000).fit(asm_feature_data_withlabel.drop(["ID","Class"], axis=1),asm_feature_data_withlabel["Class"])
#Creating dataframe for 1000 best features
df_scores=pd.DataFrame(top_k_features.scores_)
df_columns=pd.DataFrame(asm_feature_data.columns[1:])
feature_score_df=pd.concat([df_columns,df_scores],axis=1)
feature_score_df.columns=["Feature_Name","Feature_Score"]
#Let's look at the top 1000 features along with their scores + Save the feature score DF
feature_score_df=feature_score_df.nlargest(1000,"Feature_Score")
feature_score_df.to_csv(data_path+"asm_feature_top_k.csv", index=None)
feature_score_df = pd.read_csv(data_path + "asm_feature_top_k.csv")
feature_score_df.head()
Out[11]:
| Feature_Name | Feature_Score | |
|---|---|---|
| 0 | mov mov | 2.282553e+07 |
| 1 | push push | 1.450105e+07 |
| 2 | mov mov mov | 1.381146e+07 |
| 3 | push mov | 9.252609e+06 |
| 4 | push call | 9.111416e+06 |
In [12]:
#Getting top 1000 columns that have high value
cols_to_recover = list(feature_score_df["Feature_Name"])
cols_to_recover.insert(0,"ID")
#Getting the redused version of the dataframe
reduced_asm_feature_df=asm_feature_data[cols_to_recover]
reduced_asm_feature_df.head()
Out[12]:
| ID | mov mov | push push | mov mov mov | push mov | push call | imul mov | mov imul | mov push | call mov | mov call | push push push | nop nop | pop pop | mov mov imul | imul imul | imul mov mov | mov imul mov | lea push | push push call | jz mov | nop mov | push lea | cmp jz | mov nop | jmp mov | imul nop | mov cmp | mov jmp | push call mov | nop xor | nop imul | mov jz | mov push push | mov lea | cmp mov | xor nop | mov pop | mov call mov | push mov push | ... | xchg push pop | dec sub | pop call pop | dec cmp mov | xchg xchg push | jz cmp lea | lea mov inc | jmp xor jmp | movzx push call | inc pop pop | xor mov jz | lea lea call | movzx push movzx | lea sub mov | mov shr xor | add add dec | add cmp jnb | push inc | jz movzx movzx | inc mov jmp | retn xchg | dec retf | inc retn mov | mov retf mov | cmp xor mov | dec lea | pop or | cmp xor pop | xchg nop nop | cmp jnb jmp | mov jz jmp | or movzx | inc jmp xor | xor mov jmp | jmp push jmp | inc xor | movzx movzx movzx | lea mov jz | cmp jz dec | xor pop mov | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 1747 | 1594 | 504 | 577 | 678 | 50 | 44 | 843 | 969 | 497 | 729 | 0 | 202 | 12 | 2 | 14 | 28 | 35 | 496 | 278 | 0 | 45 | 6 | 0 | 192 | 0 | 285 | 165 | 395 | 0 | 0 | 443 | 515 | 153 | 402 | 0 | 202 | 333 | 80 | ... | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 7 | 0 | 0 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 1157 | 114 | 643 | 160 | 157 | 19 | 23 | 240 | 98 | 121 | 29 | 1 | 62 | 19 | 1 | 12 | 14 | 29 | 19 | 15 | 4 | 74 | 22 | 3 | 32 | 0 | 74 | 34 | 18 | 0 | 0 | 12 | 24 | 56 | 111 | 0 | 66 | 52 | 29 | ... | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 4 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 0 | 181 | 0 | 26 | 118 | 0 | 0 | 0 | 2 | 0 | 74 | 0 | 2 | 0 | 0 | 0 | 0 | 98 | 86 | 0 | 0 | 36 | 28 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3 | 01kcPWA9K2BOxQeS5Rju | 25 | 26 | 4 | 10 | 27 | 0 | 0 | 14 | 18 | 6 | 10 | 0 | 5 | 0 | 0 | 0 | 0 | 19 | 10 | 5 | 0 | 15 | 3 | 0 | 5 | 0 | 6 | 3 | 10 | 0 | 0 | 8 | 6 | 5 | 3 | 0 | 4 | 3 | 2 | ... | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 01SuzwMJEIXsK7A8dQbl | 1212 | 27 | 436 | 221 | 121 | 0 | 1 | 303 | 98 | 2 | 7 | 0 | 2 | 0 | 0 | 0 | 0 | 12 | 10 | 65 | 0 | 7 | 77 | 0 | 57 | 0 | 144 | 51 | 96 | 0 | 0 | 0 | 10 | 6 | 68 | 0 | 22 | 0 | 195 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
5 rows × 1001 columns
In [0]:
#Saving the dataframe
reduced_asm_feature_df.to_csv(data_path+"reduced_asm_feature_df.csv",index=None)
print("Saved the Reduced dataframe")
Saved the Reduced dataframe
5.9 Merging all the data and normalizing it¶
In [19]:
bytes_unigram_data = pd.read_csv(data_path + "result_with_size.csv").drop('Unnamed: 0', axis=1)
bytes_bigram_data = pd.read_csv(data_path + "reduced_byte_bigram_df.csv")
asm_unigram_data = pd.read_csv(data_path + "asmoutputfile.csv")
asm_image_data = pd.read_csv(data_path + "image_features.csv")
asm_feature_data = pd.read_csv(data_path + "reduced_asm_feature_df.csv")
combined_data = bytes_bigram_data
all_dataframes = [asm_unigram_data, asm_image_data, asm_feature_data, bytes_unigram_data]
for data in all_dataframes:
combined_data=pd.merge(combined_data,data,on="ID",how="left")
print(combined_data.shape)
combined_data.head(3)
(10868, 3111)
Out[19]:
| ID | ?? ?? | cc cc | 00 00 | ff ff | 00 ff | ff 00 | 02 02 | 10 10 | f0 f0 | 06 06 | 04 04 | f0 01 | 01 f0 | 03 03 | 90 f7 | 14 14 | 18 18 | 01 00 | 46 46 | 01 01 | 33 33 | 20 20 | 0b 0b | 4d 4d | 0f 0f | 53 53 | 5d 5d | 48 48 | fd 8b | 44 24 | 56 56 | fc fc | 75 75 | 3b 3b | 59 59 | 5e 5e | c7 c7 | 45 45 | 55 55 | ... | db | dc | dd | de | df | e0 | e1 | e2 | e3 | e4 | e5 | e6 | e7 | e8 | e9 | ea | eb | ec | ed | ee | ef | f0 | f1 | f2 | f3 | f4 | f5 | f6 | f7 | f8 | f9 | fa | fb | fc | fd | fe | ff | ?? | size | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 1819 | 99 | 274425 | 876 | 1906 | 1502 | 74 | 98 | 84 | 93 | 97 | 11 | 4 | 103 | 5 | 79 | 102 | 1791 | 98 | 76 | 73 | 78 | 69 | 88 | 90 | 78 | 99 | 104 | 7 | 404 | 78 | 86 | 71 | 89 | 100 | 80 | 97 | 85 | 93 | ... | 3438 | 2773 | 2778 | 3467 | 2753 | 3097 | 3504 | 2764 | 2746 | 2913 | 3179 | 3088 | 2678 | 4710 | 3034 | 3127 | 3409 | 3571 | 2669 | 3523 | 3476 | 3685 | 3138 | 2727 | 2808 | 3209 | 3233 | 3271 | 2804 | 3687 | 3101 | 3211 | 3097 | 2758 | 3099 | 2759 | 5753 | 1824 | 4.234863 | 9 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 8580 | 732 | 21075 | 4861 | 1036 | 2202 | 23 | 195 | 21 | 8 | 2 | 1 | 0 | 11 | 3 | 2 | 0 | 829 | 3 | 157 | 1 | 57 | 7 | 4 | 0 | 29 | 0 | 14 | 5 | 78 | 20 | 18 | 0 | 2 | 52 | 0 | 7 | 1 | 0 | ... | 547 | 617 | 452 | 504 | 449 | 503 | 400 | 401 | 797 | 497 | 353 | 368 | 312 | 7506 | 6878 | 394 | 6910 | 8206 | 223 | 385 | 442 | 6813 | 484 | 424 | 352 | 519 | 265 | 290 | 451 | 6536 | 439 | 281 | 302 | 7639 | 518 | 17001 | 54902 | 8588 | 5.538818 | 2 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 459 | 7 | 16798 | 2358 | 3076 | 2960 | 4 | 5 | 3 | 81 | 68 | 19 | 21 | 1 | 5 | 3 | 7 | 597 | 6 | 71 | 3 | 1 | 9 | 3 | 6 | 5 | 5 | 3 | 39 | 2 | 64 | 4 | 59 | 6 | 77 | 6 | 7 | 8 | 5 | ... | 2572 | 2565 | 2675 | 2473 | 2642 | 2456 | 2728 | 2369 | 2552 | 2746 | 2511 | 2246 | 2454 | 22304 | 2514 | 2804 | 2660 | 3042 | 2554 | 2520 | 2755 | 2619 | 2491 | 2537 | 2409 | 2456 | 2685 | 2598 | 2325 | 2358 | 2242 | 2885 | 2863 | 2471 | 2786 | 2680 | 49144 | 468 | 3.887939 | 9 |
3 rows × 3111 columns
In [22]:
from sklearn.feature_selection import SelectKBest, chi2
top_k_features=SelectKBest(chi2, k=1800).fit(combined_data.drop(["ID","Class"], axis=1),combined_data["Class"])
#Creating dataframe for 1800 best features
df_scores=pd.DataFrame(top_k_features.scores_)
df_columns=pd.DataFrame(combined_data.columns[1:-1]) #1 to -1 so as not to include class and ID
feature_score_df=pd.concat([df_columns,df_scores],axis=1)
feature_score_df.columns=["Feature_Name","Feature_Score"]
#Let's look at the top 1000 features along with their scores + Save the feature score DF
feature_score_df=feature_score_df.nlargest(1800,"Feature_Score")
feature_score_df.to_csv(data_path+"final_feature_names.csv", index=None)
feature_score_df.head()
Out[22]:
| Feature_Name | Feature_Score | |
|---|---|---|
| 0 | ?? ?? | 1.008849e+10 |
| 3107 | ?? | 1.008834e+10 |
| 1006 | .rdata: | 4.867531e+09 |
| 1004 | .data: | 7.456412e+08 |
| 2851 | 0 | 3.962964e+08 |
In [23]:
#Getting top 1000 columns that have high value
cols_to_recover = list(feature_score_df["Feature_Name"])
cols_to_recover.insert(0,"ID")
cols_to_recover.append("Class")
#Getting the redused version of the dataframe
final_feature_df=combined_data[cols_to_recover]
final_feature_df.head()
Out[23]:
| ID | ?? ?? | ?? | .rdata: | .data: | 0 | cc cc | cc | .text: | 2 | 00 00 | 4 | 6 | ff | 3 | 12 | 10 | 30 | 33 | 11 | 13 | 20 | 1 | 32 | 23 | 22 | 31 | 21 | :dword | mov | 0b | 0f | 18 | 8b | 8 | 14 | 5 | 0c | 7 | push | ... | mov or jz | mov push xor | add mov add | jmp mov or | jnb mov | jmp mov call | lea lea mov | retn push call | jmp call | mov mov or | mov add call | xor lea movzx | jz cmp cmp | push xor cmp | xor push lea | add call mov | push sub | pop jz mov | push pop mov | retf retf | inc cmp mov | mov xor jmp | call jmp xor | mov xor pop | lea movzx movzx | call xor pop | add add mov | lea call cmp | mov inc cmp | cmp call mov | add xor | lea add | push push jmp | push retn push | push mov add | mov mov shl | sub jz sub | push jz | sub mov push | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01azqd4InC7m9JpocGv5 | 1819 | 1824 | 1794 | 1366754 | 601905 | 99 | 3105 | 22430 | 2816 | 274425 | 3345 | 3650 | 5753 | 3832 | 3459 | 3269 | 3287 | 3029 | 2740 | 2767 | 2990 | 3905 | 3417 | 4295 | 2826 | 2878 | 3175 | 4371 | 5295 | 3546 | 3032 | 3139 | 5277 | 2965 | 3354 | 3242 | 4038 | 3201 | 3208 | ... | 3 | 12 | 8 | 5 | 0 | 2 | 12 | 2 | 2 | 60 | 8 | 0 | 0 | 0 | 0 | 43 | 78 | 5 | 0 | 0 | 1 | 5 | 2 | 1 | 0 | 0 | 19 | 2 | 1 | 28 | 17 | 8 | 0 | 0 | 5 | 14 | 0 | 2 | 25 | 9 |
| 1 | 01IsoiSMh5gxyDYTl4CB | 8580 | 8588 | 26405 | 24568 | 39755 | 732 | 1359 | 109939 | 7249 | 21075 | 8663 | 8420 | 54902 | 7186 | 352581 | 8869 | 497 | 7692 | 228 | 187 | 12372 | 8337 | 581 | 296 | 196 | 420 | 309 | 1446 | 2190 | 6649 | 6869 | 6735 | 13277 | 9291 | 12034 | 6844 | 8660 | 7589 | 569 | ... | 0 | 15 | 5 | 0 | 12 | 0 | 6 | 9 | 0 | 35 | 0 | 0 | 0 | 0 | 11 | 0 | 20 | 0 | 0 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 12 | 0 | 1 | 4 | 26 | 5 | 0 | 0 | 2 | 16 | 0 | 1 | 5 | 2 |
| 2 | 01jsnpXSAlgw6aPeDxrU | 459 | 468 | 1093 | 662 | 93506 | 7 | 2641 | 68883 | 2568 | 16798 | 8925 | 9007 | 49144 | 2438 | 2301 | 2782 | 2381 | 2313 | 2611 | 2706 | 2575 | 9542 | 2703 | 2565 | 2801 | 2731 | 2482 | 903 | 31 | 2669 | 2487 | 2677 | 22038 | 9107 | 2968 | 9330 | 9113 | 2342 | 442 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 0 | 3 | 0 | 5 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 7 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 3 | 01kcPWA9K2BOxQeS5Rju | 13932 | 13940 | 323 | 57 | 21091 | 22 | 433 | 744 | 726 | 10417 | 1257 | 550 | 7998 | 817 | 389 | 1067 | 834 | 1317 | 407 | 423 | 926 | 1213 | 679 | 366 | 371 | 1047 | 350 | 137 | 89 | 445 | 705 | 520 | 4862 | 1078 | 644 | 625 | 808 | 523 | 81 | ... | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 |
| 4 | 01SuzwMJEIXsK7A8dQbl | 9000 | 9008 | 0 | 4595 | 19764 | 31 | 285 | 10368 | 302 | 16271 | 559 | 262 | 2199 | 433 | 240 | 447 | 283 | 322 | 242 | 236 | 418 | 710 | 260 | 282 | 334 | 321 | 441 | 1220 | 2535 | 226 | 272 | 348 | 1330 | 422 | 437 | 410 | 406 | 249 | 402 | ... | 0 | 0 | 4 | 0 | 15 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 28 | 0 | 0 | 1 | 8 |
5 rows × 1802 columns
In [24]:
#Saving the dataframe
final_feature_df.to_csv(data_path+"final_feature_df.csv",index=None)
print("Saved the Reduced dataframe")
Saved the Reduced dataframe
In [25]:
data_for_normalization = final_feature_df.drop(['ID', 'Class'], axis=1)
from sklearn.preprocessing import MinMaxScaler
data_for_normalization = MinMaxScaler().fit_transform(data_for_normalization)
data_for_normalization.shape
Out[25]:
(10868, 1800)
In [0]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(data_for_normalization, final_feature_df['Class'], stratify=final_feature_df['Class'], test_size=0.20)
X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train, stratify=y_train, test_size=0.20)
5.10 Applying XGBoost on the combined data¶
In [27]:
x_cfl=XGBClassifier()
prams={
'learning_rate':[0.01,0.03,0.05,0.1,0.15,0.2],
'n_estimators':[100,200,500,1000,2000],
'max_depth':[3,5,10],
'colsample_bytree':[0.1,0.3,0.5,1],
'subsample':[0.1,0.3,0.5,1]
}
random_cfl=RandomizedSearchCV(x_cfl,param_distributions=prams,verbose=10,n_jobs=-1,)
random_cfl.fit(X_train,y_train)
Fitting 3 folds for each of 10 candidates, totalling 30 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers. [Parallel(n_jobs=-1)]: Done 5 tasks | elapsed: 45.8min [Parallel(n_jobs=-1)]: Done 10 tasks | elapsed: 53.7min [Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 73.2min [Parallel(n_jobs=-1)]: Done 27 out of 30 | elapsed: 85.9min remaining: 9.5min [Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 92.7min finished
Out[27]:
RandomizedSearchCV(cv='warn', error_score='raise-deprecating',
estimator=XGBClassifier(base_score=0.5, booster='gbtree',
colsample_bylevel=1,
colsample_bynode=1,
colsample_bytree=1, gamma=0,
learning_rate=0.1, max_delta_step=0,
max_depth=3, min_child_weight=1,
missing=None, n_estimators=100,
n_jobs=1, nthread=None,
objective='binary:logistic',
random_state=0, reg_al...
seed=None, silent=None, subsample=1,
verbosity=1),
iid='warn', n_iter=10, n_jobs=-1,
param_distributions={'colsample_bytree': [0.1, 0.3, 0.5, 1],
'learning_rate': [0.01, 0.03, 0.05, 0.1,
0.15, 0.2],
'max_depth': [3, 5, 10],
'n_estimators': [100, 200, 500, 1000,
2000],
'subsample': [0.1, 0.3, 0.5, 1]},
pre_dispatch='2*n_jobs', random_state=None, refit=True,
return_train_score=False, scoring=None, verbose=10)
In [29]:
random_cfl.best_params_
Out[29]:
{'colsample_bytree': 1,
'learning_rate': 0.1,
'max_depth': 10,
'n_estimators': 1000,
'subsample': 0.3}
In [6]:
x_cfl=XGBClassifier(n_estimators=1000,subsample=0.3,learning_rate=0.1,colsample_bytree=1,max_depth=10)
x_cfl.fit(X_train,y_train)
c_cfl=CalibratedClassifierCV(x_cfl,method='sigmoid')
c_cfl.fit(X_train,y_train)
predict_y = c_cfl.predict_proba(X_train)
print ('train loss',log_loss(y_train, predict_y))
predict_y = c_cfl.predict_proba(X_cv)
print ('cv loss',log_loss(y_cv, predict_y))
predict_y = c_cfl.predict_proba(X_test)
print ('test loss',log_loss(y_test , predict_y))
train loss 0.008769448542843082 cv loss 0.01035502615813827 test loss 0.00948338358160774
6. Conclusion¶
In [0]:
from prettytable import PrettyTable
x = PrettyTable()
x.field_names = ["Model", "Feature","Train","CV","Test"]
In [8]:
x.add_row(["KNN","unigram byte features",0.078,0.22,0.24])
x.add_row(["Logistic Regression","unigram byte features",0.498,0.549,0.528])
x.add_row(["Random Forest","unigram byte features",0.026,0.087,0.085])
x.add_row(["XGBoost","unigram byte features",0.022,0.093,0.079])
x.add_row(["XGBoost with Hyperparameter Tuning","unigram byte features",0.022,0.092,0.078])
x.add_row(["KNN","asm file features",0.047,0.095,0.089])
x.add_row(["Logistic Regression","asm file features",0.396,0.424,0.415])
x.add_row(["Random Forest","asm file features",0.011,0.049,0.057])
x.add_row(["XGBoost","asm file features",0.011,0.056,0.049])
x.add_row(["XGBoost with Hyperparameter Tuning","asm file features",0.010,0.050,0.048])
x.add_row(["Random Forest","asm file features + byte unigram features",0.016,0.035,0.040])
x.add_row(["XGBoost","asm file features + byte unigram features",0.011,0.031,0.032])
x.add_row(["XGBoost with Hyperparameter Tuning","asm file features + byte unigram features",0.012,0.034,0.031])
x.add_row(["XGBoost with Hyperparameter Tuning","asm file features + byte unigram and bigram features + asm image features",0.010,0.041,0.025])
x.add_row(["XGBoost with Hyperparameter Tuning","asm file features + byte unigram and bigram features + asm image features + asm bi-trigram features on opcodes",0.0087,0.0103,0.0094])
print(x)
+------------------------------------+----------------------------------------------------------------------------------------------------------------+--------+--------+--------+ | Model | Feature | Train | CV | Test | +------------------------------------+----------------------------------------------------------------------------------------------------------------+--------+--------+--------+ | KNN | unigram byte features | 0.078 | 0.22 | 0.24 | | Logistic Regression | unigram byte features | 0.498 | 0.549 | 0.528 | | Random Forest | unigram byte features | 0.026 | 0.087 | 0.085 | | XGBoost | unigram byte features | 0.022 | 0.093 | 0.079 | | XGBoost with Hyperparameter Tuning | unigram byte features | 0.022 | 0.092 | 0.078 | | KNN | asm file features | 0.047 | 0.095 | 0.089 | | Logistic Regression | asm file features | 0.396 | 0.424 | 0.415 | | Random Forest | asm file features | 0.011 | 0.049 | 0.057 | | XGBoost | asm file features | 0.011 | 0.056 | 0.049 | | XGBoost with Hyperparameter Tuning | asm file features | 0.01 | 0.05 | 0.048 | | Random Forest | asm file features + byte unigram features | 0.016 | 0.035 | 0.04 | | XGBoost | asm file features + byte unigram features | 0.011 | 0.031 | 0.032 | | XGBoost with Hyperparameter Tuning | asm file features + byte unigram features | 0.012 | 0.034 | 0.031 | | XGBoost with Hyperparameter Tuning | asm file features + byte unigram and bigram features + asm image features | 0.01 | 0.041 | 0.025 | | XGBoost with Hyperparameter Tuning | asm file features + byte unigram and bigram features + asm image features + asm bi-trigram features on opcodes | 0.0087 | 0.0103 | 0.0094 | +------------------------------------+----------------------------------------------------------------------------------------------------------------+--------+--------+--------+
Steps followed during the case study
- Initially Exploratory Data Analysis has been performed on byte files.
- On top of that unigram features are extracted from byte files and machine learning models are applied on them.
- Then the asm fileswere preprocessed and unigram features of different segments were applied on them.
- On top of the asm features machine learning models are applied.
- Then the byte features and asm features were combined and machine learning models were applied on top of them.
- On further analysis byte bigram features, asm image features, asm opcode(bi-tri gram) features are extracted from files.
- XGBoost is then applied on top of the features and the results of all the models can be seen above.
References